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How to Audit International Facebook Advertising? A 37-Item Checklist

This is a joint article written with Mr. Tommi Salenius who works as a digital marketing specialist at Parcero Marketing Partners.

Introduction

Facebook advertising is a powerful form of online marketing for many purposes ranging from direct response campaigns to brand visibility and awareness. However, the competition in the ad platform is increasing every year, as companies are increasing their investments due to the fact that Facebook advertising, relatively speaking, works very well.

Figure 1 shows how Facebook’s revenue, comprising almost exclusively from advertising, has grown during the last nine years. Last year, almost $40,000,000,000 (that’s forty billion dollars) were spent on Facebook ads.

Figure 1. Facebook worldwide ad revenue statistics from Statista.com.

Increasing budgets imply increasing competition which means that in order to maintain the same visibility, advertisers need to increase their bids. For this purpose, in order to make profit in Facebook, advertisers need to continuously optimize their accounts.

To illustrate the power of Facebook advertising for online sales, Figure 2 shows an example from profitable Facebook account targeting direct online sales.

Figure 2. Example from Facebook account targeting direct online sales.

In this example, every euro invested in Facebook ads has generated direct online sales worth of €10. This means that with budget of €100,000 you can make sales worth of €1,000,000 if your target group is large enough and there is demand for your product (assuming that the sale grow linearly, of course).

The case of international Facebook advertising

Facebook is also one of the best choices to advertise globally, given its user base of more than two billion monthly active users (source: Statista.com).

Using the Locations feature in Facebook Ads targeting, several geographic targeting criteria can be chosen:

  • worldwide (type “Worldwide”)
  • country group or geographic region (e.g., type “in Europe”)
  • free trade area (e.g., type in “GCC, the Gulf Cooperation Council”)
  • sub-regions within a country (e.g., type in “Washington”)
  • other features (e.g., type in “Emerging markets”).

Figure 3 illustrates the Facebook targeting interface.

Figure 3. Targeting interface in Facebook Ads.

At the time of writing (October, 2018), the global targeting options in Facebook include the following:

Country groups

  • Africa
  • Asia
  • Caribbean
  • Central America
  • Europe
  • North America
  • Oceania
  • South America

Free Trade Areas

  • AFTA (ASEAN Free Trade Area)
  • APEC (Asia-Pacific Economic Cooperation)
  • CISFTA (Commonwealth of Independent States Free Trade Area)
  • EEA (European Economic Area)
  • GCC (Gulf Cooperation Council)
  • MERCOSUR
  • NAFTA (North American Free Trade Agreement)

Other Areas

  • Android app countries (paid)
  • Android app countries (all)
  • Emerging markets
  • Euro area
  • iTunes app store countries

Despite the tremendous potential of global advertising in Facebook Ads, companies often do not exploit this potential to the fullest. Moreover, we have observed that large international accounts tend to be messy and not well optimized. Therefore, in the following, we provide a checklist that can be used to audit such international Facebook Ads accounts.

Checklist for auditing international Facebook advertising

Here is a checklist for auditing Facebook paid advertising for international companies. This checklist is a concrete tool that can be used to evaluate your Facebook ad account’s current performance and identifying development areas that can get you toward desired results. There will be four sections: A) Account setup, B) Ad campaigns, C) Organic content, and D) International aspect.

Section A: Account setup

1. Is Facebook Business Manager activated? Benefit: Gain more control over user rights and possibility to operate with partners.

2. Is Facebook pixel is installed and configured? Benefit: Makes it possible to track business-related goals, for example, sales, visitors, blog reading times etc.

3. Is additional software being used besides Facebook Ad Platform? Benefit: Specific tools (e.g. Smartly, AdEspresso, Qwaya) can enhance Facebook performance by providing special features. If they are not used, at least they should be explored.

4. Is international Facebook page feature acclaimed? Benefit: This feature enables unified follower count for country pages but separated content on the country basis.

5. Is ‘business locations’ option used? Benefit: This feature enables to input specific geographic business locations.

Section B: Ad campaigns

6. Are Facebook campaign goals aligned with business goals? Benefit: The campaign goals (e.g. reach, engagement, traffic, sales, leads) should be traced back to overall marketing strategy to ensure they match what is wanted.

7. What is Facebook strategy of the current campaigns? Benefit: In auditing, it is useful to mentally classify the types of campaigns used in the ad account. These can include:

  • technology oriented — e.g., using dynamic ads for advanced targeting
  • content oriented — e.g., using creative concepts to get noticed
  • systematic advertising — i.e., customers need to be reminded regularly
  • ad hoc campaigns — i.e. running ads sporadically without clear purpose

8. Is there something that works already? Benefit: Verifying what already works enables to focus efforts on proven areas (e.g., some campaigns generate sales with low cost, data shows that specific creatives are working, different demographics are responding to ads).

9. Are there budget delivery problems? Benefit: Deliver issues are a common concern in Facebook Ads. Potential reasons: low ad relevance scores, low budget or bids, or not enough conversions (minimum 100 per month), wrong optimization goal. Solutions: change your optimization goal, e.g. from purchases to link clicks, test new target groups and ads, increase budget and bids.

10. Does campaign structure follow best practices? Benefit: Clear division of campaigns provides better tracktability and optimization. There should be different campaigns for all goals: prospecting and retargeting, upselling and cross-selling, reach and sales etc.

11. What auction type is used? Benefit: Auction vs. fixed price: with auction you get better results if you beat competition.

12. What placements are used? Benefit: Performance varies across placements, therefore, they should be tested. Facebook ad platform offers these placements: Facebook, Instagram, Audience Network, and Facebook Messenger. Based on our experiments, Audience Network usually performs poorly, and Instagram is more expensive than Facebook. Moreover, Messenger ads might be thought of more annoying than other placements because they are invading the user’s private space (the inbox).

13. What ad content types have been tested? Benefit: A good account has tested various different ad types (incl. carousel, link ad, instagram story, video, image, canvas).

14. What retargeting types have been used? Benefit: A good account has applied multiple retargeting types (incl. website retargeting, email retargeting, content retargeting).

15. What levels of retargeting are utilized? Benefit: A good account is “deep retargeting”, meaning that retargeting is specified to particular section of the website (e.g., main page, category pages, products pages, blog articles, cart, upselling, cross-selling).

16. What lookalike audience types are used? Benefit: Lookalike audiences can work because they retrieve similar users by “cross-polinating” the targeted subset of users with Facebook’s known information about other users. These options should have been tested (website, email, page likes, purchased lookalikes).

17. Is A/B testing performed systematically? Benefit: A/B test are a sign of active campaign management (both ad set and ad level). Facebook Ads provides a native option for A/B testing as a special campaign type (this campaign type can be used e.g. for testing different creatives, target groups or technical settings).

18. How well are the assets structured? Benefit: Clear naming principles make it easier to analyze and optimize (e.g., are campaigns, ad sets, and ads named systematically).

19. Is UTM tagging used? Benefit: UTM parameters enable tracking visitor performance in other analytics software, such as Google Analytics. The tagging can be done manually or automatically; the main point is that it should be done.

20. What attribution model is used? Benefit: Choosing a different attribution model can drastically change the interpretation of account performance. There are two types of conversions in Facebook: view conversions and click conversions. To get a more conversative view, include only the click conversions with a short attribution window (e.g., 1 day). To get a more rosy picture, include view conversions with a long attribution window (e.g., 28 days). There is no absolutely right or wrong attribution model.

21. Is dynamic advertising used? Benefits:

  • dynamic advertising can be used both in retargeting and in new customer acquisition
  • it offers wide range of options, if technical setup is made correctly, e.g., automated price promotions

22. Is advanced configuration of dynamic advertising used? Benefit: This is underused, yet highly potential feature of Facebook Ads — it enables to customize automatic advertising (e.g., prefer products with high gross margin, geographically show right products for right areas).

23. Are rules used for optimization? Benefit: Rules enable the monitoring and automatic response to business critical conditions (e.g., notification from data anomalies, adjusting budget based on results etc.).

24. Is the budget spent effectively? Benefit: Facebook Ads can waste budget, but there can also be much potential for upscaling the spend — based on performance metrics, one should analyze if the budget should be decrease/increased, what is the potential reach of target groups, how well are those target groups reached, and with what impression frequency.

25. What bid strategy is used? Benefit: A good account has tested several options, including: Lowest cost (standard), lowest cost with bid cap (risk of delivery issues), or Target cost (can be used for scaling up the budget).

Section C: Organic content

26. Is there enough quality content to be believable on the eyes of customers if they visit the Facebook page? Benefit: Visitors may want to check the quality of the page. Having little or no organic content creates mistrust.

27. How active are the Facebook followers of the page? Benefit: There can be a possibility to get insights from followers or turn their enthusiasm into more business. Engagement rate is a good metric, i.e. divide post responses by post impressions.

28. Is organic content reaching the target group? Benefit: If not, maybe it should be advertised. Many Facebook pages produce fairly good content that reaches nobody organically.

29. Is there point of focusing organic content or paid advertising? Benefit: The strategic roles of organic and paid should be addressed. What is the role of organic content? What is the role of paid advertising? Note: multiple ads can be advertised and A/B tested without publishing these on the news feed.

Section D: International aspect

30. Are the ads translated? When doing advertising to e.g. 10 countries with different languages, the ads should also be communicated in 10 different languages. Note that one country can contain multiple language groups, requiring localization even within a single country.

31. Is campaign structure supporting multiple languages? Each language should have been placed in separate target groups. For example, campaign could be name after the country, and it should contain different ad groups for each languages.

32. Is there enough budget to advertise internationally to all target groups? If you are targeting several countries, cities, and languages, these all need different budgets. In order to make impact, it is not usually wise to divide budget into too small pieces.

33. Is there other localization besides translation? Often, an error is made to assume localization is only about language. However, it is also about culture, customs, and ethnicity. For example, value propositions of communicated benefits may be entirely different when the same product is promoted to culturally different target groups (e.g., collectivity-individuality aspect might differ). Another example is that imagery matters for ethnic match between the target audience and people shown in the ads.

34. Have the country-basis legal restrictions been taken into consideration? E.g. different countries have different restrictions for promoting alcohol products, and European countries have strict orders for handling the data according to GDPR protocol.

35. How do normalized metrics vary by countries? Compare performance by normalized metrics (e.g., ROI), because that adjusts for variation between the markets. For example, Facebook Ads bids can be ten times more expensive in the US than in Vietnam. Similarly, purchase power differs so avg. conversion value can be one tenth in Vietnam, meaning that advertising would be equally profitable. To account for this, use normalized metrics, such as ROI or ROAS.

36. What are the city-level performance differences? Another common mistake is to assume that country is detailed enough segmentation criteria for performance differences. However, performance can vary greatly by city, e.g. in big countries like China or US. Moreover, rural areas can differ compared to city areas because people’s tastes, values, and behavior is different. To accommodate for this, Facebook advertisers should segment by city in addition to country (e.g., compare TOP 5 cities of each country).

37. What are the segment similarities across countries? Each impression has a cost. And each impression also adds information about customer responses. However, in the Facebook Ads account the performance values are siloed across different campaigns and ad sets. Therefore, to optimize such accounts, data needs to be combined. For example, if targeting 12 countries, the performance by demographic groups can be aggregated to give more statistical power (higher reliability for found similarities and differences).

Conclusion

This list of 37 items is a good starting point for analysing any Facebook Ads account running international campaigns. Besides these steps, Facebook account level data can be used for analysis purposes to find patterns in the data. For example, making country level breakdowns is made easy in the user interface of Facebook Ads platform.

About the authors:

Tommi Salenius is a Digital Marketing Manager at Elämyslahjat.fi, a Finnish e-commerce company that sells experience gifts. Tommi also works at Parcero Marketing Partners as a Lead Digital Marketing Strategist. www.tommisalenius.com

Joni Salminen is a Digital Marketing Manager at Elämyslahjat.fi, a Finnish e-commerce company selling experience gifts. Joni is also a board member at Konvertigo Digital Agency that runs digital marketing campaigns to over 100 countries. www.jonisalminen.com

Automatic Analytics: Considerations for Building User-Oriented Data Analytics Systems

This is an unpublished exploratory study we wrote with Professor Jim Jansen for Machine Learning and Data Analytics Symposium (MLDAS2018), held in Doha, Qatar.

Change of landscape: For a long time, automation has been invading the field of marketing. Examples of marketing automation include the various scripts, rules, and software solutions that optimize pay-per-click spending, machine learning techniques utilized in targeting of display advertising (Google, 2017), automated tools that generate ad copy variations, and Web analytics platforms that automatically monitor the health of marketing performance, alerting the end users automatically in case of anomalies.

In particular, several steps of progress towards automating analytics insights are currently being made in the industry and research fronts of data analytics.

For example, there are several tools providing automated reporting functions (e.g., Google Analytics, TenScores, Quill Engage, etc.). While some of these tools require pre-configuration such as creating report templates, it is becoming more common that the tool itself chooses the relevant insights it wants to portray, and then delivers these insights to the decision makers, typically pinging via email. An example of such an approach is provided in Figure 1 that shows Quill Engage, a tool that automatically creates fluent text reports from Google Analytics data.

Figure 1: Quill Engage. The Tool Automatically Generates Fluent Reports From Google Analytics Data, And Provides Numerical Comparisons Based on Outliers And Trends.

As can be seen from Figure 1, the automatic analytics tool quickly displays key information and then aims to provide context to explain the trends in the key performance indicators.

Benefits: The benefits of automatic analytics are obvious. First of all, automation spares decision makers’ time, as they are not forced to log into systems, but receive the insights conveniently to their email inboxes and can rapidly take action. Since cognitive limitations (Tversky & Kahneman, 1974) are imposing serious constraints for decision makers dealing with ever-increasing amounts of “big data,” the need for smart tools that pre-process and mine the data at the user’s convenience are highly beneficial.

The core issue that automatic analytics is solving is complexity.

As a marketing manager, one has many platforms to manage and many campaigns to run within each platform. Multiple data sources, platforms, and metrics quickly introduce a degree of complexity that hinders effective processing of information by human beings, constrained by limitations of cognitive capacity.

In general, there are two primary use cases for business analytics: (1) deep analyses that provide strategic insights, and (2) day-to-day analyses that provide operational or tactical support. While one periodically needs to perform deep analyses on strategic matters, such as updating online marketing strategy, creating a new website structure, etc., the daily decisions cannot afford a thorough use of tens of reports and hundreds of potential metrics. That is why many reports and metrics are not used by decision makers in the industry at all.

The solution to this condition has to be automation: the systems have to direct human users’ attention toward noteworthy things. This means detecting anomalies on marketing performance, predicting their impact and presenting them in actionable format to decision makers, preferably by pinging them via email or other channels, such as SMS. The systems could even directly create tasks and push them to project management applications like Trello. A requisite to automatic analytics should therefore be the well-known SMART formula, meaning that Specific, Measurable, Appropriate, Realistic and Timely goals (Shahin & Mahbod, 2007). Through this principle, decision makers are able to rapidly turn insights into action.

Interfaces for automatic analytics: To accomplish the goal of automatic analytics, one trending area of is natural language systems, where users find the information by asking the system questions in free format. For example, previously, Google Analytics had a feature called Intelligence Events, which detected anomalies. Currently, Google provides automatic insights via a mobile app, in which the user can ask the system in natural language to provide information. An example of this is provided in Figure 2.

Figure 2: Screenshot from Google Analytics Android App, Showing the Functionality of Asking Questions From the Analytics System.

However, even asking the system requires effort and prior knowledge. For example, what if the question is not relevant or misses an important trend in the data? For such cases, the system must anticipate, and in fact analyze the data beforehand. This form of “intelligent anticipation” is a central feature in automatic analytics systems.

Examples: In the following, we provide some examples of current state-of-the-art tools of automatic analytics. We then generalize some principles and guidelines based on an overview of these tools.

First, in Figure 3, we present a screenshot from email sent by TenScores, a tool that automatically scans Quality Scores for Google AdWords campaigns.

Figure 3: TenScores, the Automatic Quality Score Monitoring Tool.

In search-engine advertising, Quality Scores are important because they influence the click prices paid by the advertisers (Jansen & Schuster, 2011; Salminen, 2009). In this particular case, the tool informs when there is a change in the average Quality Score of the account.

From a user experience perspective, the threshold to alerting the user is set to very low change, resulting in many emails sent to the users. This highlights the risk of automation becoming “spammy,” leading into losing user interest. The correct threshold should be set experimentally, e.g., according to open rates by experimenting with different increments of messaging frequency and impact thresholds.

In Figure 4, we can see a popular Finnish online marketplace, Tori.fi. Tori sends automatic emails to its corporate clients, showing how their listings have performed compared to previous period, and enabling the corporate clients to take direct action from within the email.

From example, one can click the blue button and the particularly listing which is not performing well, is boosted. In addition, there is a separate section (not visible from the screenshot) showing the best performing listings.

Figure 4: Tori’s Marketplace Insights Automatically Delivered to Inbox.

Risks: There are also risks associated with automatic analytics. For example, In search-engine advertising, brands are bidding against one another (Jansen & Schuster, 2011). Thus, an obvious step to further optimize their revenue by providing transparent auction information is Google sending automatic emails when the relative position (i.e., competitiveness) of a brand decreases, prompting advertisers to take action.

This potential scenario also raises questions about morality and ethics of automated analytics, especially in click auctions where the platform owners have an incentive to recommend actions that inflate click prices (Salminen, 2009). For example, in another online advertising platform, Bing Ads, the “Opportunities” feature gives suggestions marketers can implement in a click of a button. However, many of these suggestions relate to increasing the bid prices (see Figure 5).

Figure 5: An example of Bing Ads Recommending to Increase Keyword Bid.

If the default recommendation is always to raise bids, the feature does not add value to end user but might in fact destroy it. From an end user point of view, therefore, managers are encouraged to take recommendations with a grain of salt in such cases. From a research point of view, it is an interesting question to find out how much the automatic recommendations drive user actions.

Discussion: The current situation is that marketing optimization consist of various micro-tasks that are inter-connected and require analytics skills and creativity to be solved in an optimal way. The role of automated analytics, at least with the current maturity of technology, is pre-filtering this space of potential tasks into a number that is manageable to human decision makers, and, potentially, assigning the tasks priority according to their predicted performance impact.

In this scenario, humans are still needed to make the final decisions. The human decides which suggestions or insights to act upon. Nevertheless, the prospect of automatic filtering and sorting is highly beneficial in maneuvering the fragmented channel and campaign landscape taking place in practical online marketing work.

Practical guidelines:

  • As each vertical has its own KPIs, metrics and questions, there is a requirement of using many tools. For example, search-engine optimizers require drastically different information than display advertisers, and therefore it makes no sense to create a single solution. Instead, an organization should derive the tools from its business objectives and based on the specific information needed to achieve them.
  • An example of fine-grained automatic analytics is TenScores that only specializes on monitoring one metric in one channel (Quality Score in Google AdWords). Their approach makes sense because Quality Score is such an important metric for keyword advertisers and its optimization involves a complexity, enabling TenScores to provide in-depth recommendations that are valuable to end users.

However, even though the tools may be channel-specific, their operating principles can be similar. For example, stream filtering and anomaly detection algorithms are generalizable to many types of data, and thus have wide applicability. Moreover,

  • setting the frequency threshold to pinging decision makers is a key issue that should be experimented with when designing automatic analytics systems.

Even if there is automation, it is too early to speak of real artificial intelligence. The current systems always have manually set parameters and thresholds, and miss important things that are clear for individuals. For example, the previously shown Quill Engage cannot provide an explanation why the sales dropped when going from December to January — yet, this is apparent to any individual working in the gift business: Christmas season was the reason.

Implications for developers of automatic analytics systems: Developers of various analytics systems should no longer expect that their users log in to the system to browse reports. Instead, the critical information needs to be automatically mined and sent to decision makers in an actionable format (cf. SMART principle). There is already a considerable shift in the industry to this direction which will only be emphasized as customers realize the benefits of automatic analytics. Thus, we believe the future of analytics is more about detecting anomalies and opportunities, and giving decision makers easy choices to act upon. Of course, there are also new concerns in this environment, such as biased recommendations by online ad platforms – is the system advising you to increase bids because it maximizes your profit or because it increases the owner’s revenue?

Conclusion: Analytics software providers are planning to move toward the direction of providing automated insights, and researchers should follow suite. Open questions are many, especially relating to users’ interaction with automatic analytics insights: how responsive are users to the provided recommendation? What information do the users require? What actions do users take based on the information? We expect interesting studies in this field in the near future.

References

Google. (2017). Introducing Smart display campaigns. Retrieved February 12, 2018, from https://adwords.googleblog.com/2017/04/introducing-smart-display-campaigns.html

Jansen, B. J., & Schuster, S. (2011). Bidding on the buying funnel for sponsored search and keyword advertising. Journal of Electronic Commerce Research, 12(1), 1–18.

Lee, K.-C., Jalali, A., & Dasdan, A. (2013). Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising. In Proceedings of the Seventh International Workshop on Data Mining for Online Advertising (p. 1:1–1:9). New York, NY, USA: ACM. https://doi.org/10.1145/2501040.2501979

Salminen, J. (2009). Power of Google: A study on online advertising exchange. Turku: Master’s thesis. Turku School of Economics.

Shahin, A., & Mahbod, M. A. (2007). Prioritization of key performance indicators: An integration of analytical hierarchy process and goal setting. International Journal of Productivity and Performance Management, 56(3), 226–240.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

Google Analytics: 21 Questions to Get Started

I was teaching a course called “Web & Mobile Analytics” at Aalto University back in 2015.

As a part of that course, the students conducted an analytics audit for their chosen website. I’m sharing the list of questions I made for that audit, as it’s a useful list for getting to know Google Analytics.

The questions

Choose a period to look at (e.g., the past month, three months, last year, this year… generally, the longer the better because it gives you more data). Answer the questions. The questions are organized by sections of Google Analytics.

a. Audience

  • How has the website traffic evolved during the period your inspecting? How does the traffic from that period compare to earlier periods?
  • What are the 10 most popular landing pages?
  • What are the 10 pages with the highest bounce rate AND at least 100 visits in the last month? (Hint: advanced filter)

b. Behavior

  • How does the user behavior differ based on the device they’re using? (Desktop/laptop, mobile, tablet)
  • Where do people most often navigate to from the homepage?
  • How do new and old visitors differ by behavior?
  • What is the general bounce rate of the website? Which channel has the highest bounce rate?
  • How well do the users engage with the website? (Hint: Define the metrics you used to evaluate engagement.)
  • Is there a difference in engagement between men and women?

c. Acquisition

  • How is the traffic distributed among the major sources?
  • Can you find performance differences between paid and organic channels?
  • Compare the goal conversion rate of different marketing channels to the site average. What can you discover?

d. Conversion

  • What is the most profitable source of traffic?
  • What is the best sales (or conversion, based on the number of conversions) month of the year? How would you use this information in marketing planning?
  • Which channels or sources seem most promising in terms of sales potential? (Hint: look at the channels with high CVR and low traffic)
  • Analyze conversion peaks. Are there peaks? Can you find explanation to such peaks?
  • Can you find sources that generated assisted conversions? Which sources are they? Is the overall volume of assisted conversions significant?
  • Does applying another attribution model besides the last click model alter your view on the performance of marketing channels? If so, how?

e. Recommendations

  • Based on your audit, how could the case company develop its digital marketing?
  • How could the case company’s use of analytics be developed? (E.g., what data is not available?)
  • What other interesting discoveries can you make? (Mention 2–5 interesting points.)

Answering the above questions provides a basic understanding of a goal-oriented website’s situation. In the domain of analytics, asking the right questions is often the most important (and difficult) thing.

The dashboard

In addition, the students built a dashboard for the class. Again, the instructions illustrate some useful basic functions of Google Analytics.

Build a dashboard showing the following information. Include a screenshot of your dashboard in the audit report.

Where is the traffic coming from?

  • breakdown of traffic by channel

What are the major referral sources?

  • 10 biggest referral sites

How are conversions distributed geographically?

  • 5 biggest cities by conversions

How is Facebook bringing conversions?

  • Product revenue from Facebook as a function of time

Are new visitors from different channels men or women?

  • % new sessions by channels and gender

What keywords bring in the most visitors and money?

  • revenue and sessions by keyword

If you see fit, include other widgets in the dashboard based on the key performance metrics of your company.

Conclusion

Reports and dashboards are basic functions of Google Analytics. More advanced uses include custom reports and metrics, alerts, and data importing.

Simple methods for anomaly detection in e-commerce

Anomaly is a deviation from the expected value. The main challenges are: (a) how much the deviation should be to be classified as an anomaly, and (b) what time frame or subset of data should we examine.

The simplest way to answer those questions is to use your marketer’s intuition. As an e-commerce manager, you have an idea of how big of an impact constitutes an anomaly for your business. For example, if sales change by 5% in a daily year-on-year comparison, that would not typically be an anomaly in e-commerce, because the purchase patterns naturally deviate this much or even more. However, if your business has e.g. a much higher growth going on and you suddenly drop from 20% y-o-y growth to 5%, then you could consider such a shift as an anomaly.

So, the first step should be to define which metrics are most central for tracking. Then, you would define threshold values and the time period. In e-commerce, we could e.g. define the following metrics and values:

  • Bounce Rate – 50% Increase
  • Branded (Non-Paid) Search Visits – 25% Decrease
  • CPC Bounce – 50% Increase
  • CPC Visit – 33% Decrease
  • Direct Visits – 25% Decrease
  • Direct Visits – 25% Increase
  • Ecommerce Revenue – 25% Decrease
  • Ecommerce Transactions – 33% Decrease
  • Internal Search – 33% Decrease
  • Internal Search – 50% Increase
  • Non-Branded (Non-Paid) Search Visits – 25% Decrease
  • Non-Paid Bounces – 50% Increase
  • Non-Paid Visits – 25% Decrease
  • Pageviews – 25% Decrease
  • Referral Visits – 25% Decrease
  • Visits – 33% Decrease

As you can see, this is rule-based detection of anomalies: once the observed value exceeds the threshold value in a given time period (say, daily or weekly tracking), the system alerts to e-commerce manager.

The difficulty, of course, lies in defining the threshold values. Due to changing baseline values, they need to be constantly updated. Thus, there should be better ways to detect anomalies.

Another simple method is to use a simple sliding window algorithm. This algorithm can (a) update the baseline value automatically based on data, and (b) identify anomalies based on a statistical property rather than the marketer’s intuition. The parameters for such an algorithm are:

  • frequency: how often the algorithm runs, e.g. daily, weekly, or monthly. Even intra-day runs are possible, but in most e-commerce cases not necessary (exception could be technical metrics such as server response time).
  • window size: this is the period for updating. For example, if the window size is 7 days and the algorithm is run daily, it computes that data always from the past seven days, each day adding +1 to start and end date.
  • statistical threshold: this is the logic of detecting anomalies. A typical approach is to (a) compute the mean for each metric during window size, and (b) compare the new values to mean, so that a difference of more than 2 or 3 standard deviations from the mean indicates an anomaly.

Thus, the threshold values automatically adjust to the moving baseline because the mean value is re-calculated at each window size.

How to interpret anomalies?

Note that an anomaly is not necessarily a bad thing. Positive anomalies occur e.g. when a new campaign kicks off, or the company achieves some form of viral marketing. Anomalies can also arise when a season breaks in. To mitigate such effects from showing, one can configure the baseline to represent year-on-year data instead of historical data from the current year. Regardless of whether the direction of the change is positive or negative, it is useful for a marketer to know there is a change of momentum. This helps restructure campaigns, allocate resources properly, and become aware of the external effects on key performance indicators.

Identifying opportunities that Google and Facebook can’t handle

It’s almost impossible to beat Facebook’s or Google’s algorithms in ad optimization, because they have access to individual-level data whereas the advertiser only gets aggregates, and even their supply is limited. But, there are two opportunities I see which Google and Facebook don’t handle:

1. Use of CRM data

Especially purchase history (=lifetime value), product margins (=profitability), and other customer information that can be used for user modelling or machine learning as features. But, don’t use Google Analytics for linking this data to website analytics — Google Analytics sucks, because Google keeps individual-level information (=click-stream data) for itself and only shares, again, aggregates. Use Piwik instead.

2. Use of cross-platform data

Google doesn’t have access to Facebook’s data or vice versa, but the advertiser has. Thus, you can create more comprehensive optimization models for bidding and budgeting.

How to reach B2B audiences online? That’s the question.

Introduction

It *is* indeed the question – most often focus of B2B digital marketing is on lead generation and marketing automation. Much less has been written about targeting.

Yet, B2B targeting is far from having been solved. The typical conversation between decision makers and agencies goes something like this.

Client: “All this online advertising is great – but I don’t want to reach regular people on Facebook. How can I reach managers who are interested in buying my products?”

So far the agency’s answer has been something like: “Well Sir, even the managers are using Facebook!”

Which, although true, remains as a quite shallow answer to a serious concern. Especially because the chance of finding the managers is in proportion to their prevalence in the general Facebook audience – that is, very tiny. Unless we solve the problem of targeting.

Solving the B2B targeting problem

Now, I’ve been interested in this question for a while. So far, I can see four tactics for reaching B2B audiences with digital marketing:

  1. Keyword targeting
  2. Manual display placements
  3. Advanced targeting criteria
  4. Audience looping process

Let’s explore each of these.

First, obviously we can opt for intent-based search marketing. When there is search volume, search advertising tends to outperform other digital marketing channels in terms of cost-efficiency. That’s because people are actively seeking for the products and services that show in the ads. However, the problem is that the industrial search volumes tend to be small in many verticals, leaving the impact on revenue very minimal. As such, the B2B marketer needs to look elsewhere to increase the number of leads.

Second, to get extra traffic we can go and buy media from known business press (e.g., Forbes, Wall Street Journal; you get the picture). The downsides of this approach are at least three:

a) CPM prices are often high in these venues, resulting typically in poor ROI – this is partly because the way ad space is priced relies on archaic methods, such as selling impressions, instead of modern approaches like click auctions

b) display advertising generally suffers from multiple problems, including banner blindness, ad clutter, and ad blockers. If you haven’t noticed, people don’t really like banner ads, or at least they seem to like them a lot less than text ads and Facebook Ads which are often better targeted and less intrusive

c) uncertainty – how can we know that the people of our industry are reading this publication? There may be other publications that they read, but those might not always be available to display advertisers.

Third, we can use various targeting parameters available in online platforms. For example, in Facebook and GDN you can find people by income level (e.g., top 10% of income earners) and net worth. However, here we assume that more rich people have better jobs, which is true in statistical sense but does not narrow the audience down enough to reach the B2B decision makers in our industry. Thus, it is better if we can get into direct targeting criteria, such as job position and company. And, we can.

For example, here is Facebook targeting for people who for Kone, the Finnish elevator company.

Figure 1 Targeting company employees on Facebook

In a similar vein, we can target a) industries and b) job titles on Facebook. Below are examples of both.

A. The current industries on Facebook Ads

  • Work > Industries > Management
  • Work > Industries > Administrative
  • Work > Industries > Sales
  • Work > Industries > Production
  • Work > Industries > Personal Care
  • Work > Industries > Education and Library
  • Work > Industries > Arts, Entertainment, Sports and Media
  • Work > Industries > Healthcare and Medical
  • Work > Industries > Transportation and Moving

B. Job Titles with search query ‘manager’:

  • Manager
  • Manager Employers
  • Talent manager
  • Manager (baseball)
  • Manager (association football)
  • Hotel manager
  • Sales Manager
  • Business Manager
  • Branch Manager
  • Relationship Manager
  • Marketing Manager
  • Finance Manager
  • Store Manager

For example, we could target people who work for ‘Kone’ and are ‘managers’. While this might work for corporations, targeting smaller companies is not possible because they are missing from Facebook’s database. We could also try and find the physical addresses of the corporations, and use geo-targeting to reach them – although without IP targeting (not available e.g. in Google and FB but can be purchased with big money elsewhere) it’s a shotgun approach, unless the company is located in the middle of nowhere. Overall, the problems of using targeting criteria include validation, sparsity and availability. I discuss these in the following.

a) validation – the accuracy of this data is not guaranteed, and we have no way of knowing that the ad platforms correctly classifies people. For example, I’ve seen people who most certainly don’t work for Facebook write Facebook as their employer.

b) sparsity – secondly, not that many people declare their workplace in Facebook, so the data is sparse and we don’t end up making a great number of qualified matches.

c) availability – all data is not available in all locations – e.g., Finland is missing the income level targeting.

Therein comes LinkedIn. As you can see from the following figure, LinkedIn provides several different targeting options that make it the most potential B2B marketing platform in the world.

Figure 2 LinkedIn targeting options

For example, we are likely to reach the proper Kone employees in different sub-companies, as visible from the following figure.

Figure 3 Company targeting on LinkedIn

While people may be careless about providing the correct job information on Facebook, on LinkedIn it is very rare that people would fake their job positions. Such behavior is easily captured and reported.

The downsides of LinkedIn are that a) the ad platform is less developed than those of Google and Facebook. If you’d like to rank them, ‘Google > Facebook > LinkedIn’ is the order from best to worst in terms of functionalities, although LinkedIn is rapidly catching up. Addtionally, b) the prices are considerably higher on LinkedIn than on Facebook or Google.

In the attempt to combine some of the good parts of each technique, I’ve created a simple process whereby we obtain leads through effective LinkedIn advertising and build segmented audiences for retargeting. This process is illustrated in the following figure.

Figure 4 Audience looping process

In particular, the idea is to create special landing pages that have a unique pixel configuration that corresponds to that audience — for example, you can “Managers from Mid-West US” who are targeted to a specific landing page that has unique Google and Facebook pixels with the proper description installed.

Managers from Mid-West -> Landing page A <–> Pixel A

Managers from upstate NY -> Landing page B <–> Pixel B

This way, you can segment your B2B audience further, and in retargeting through GDN, Facebook and potential other networks such as AdRoll, address them with highly tailored communication. In a similar vein, you want to cross-target to visitors you reach from those channels also on LinkedIn – see the figure.

Figure 5 Cross-retargeting

If you already have an email database (CRM, newsletter lists), you should obviously use that to build custom audiences, and then use lookalike audiences to maximize reach (called ‘Audience expansion’ on LinkedIn).

Since all the platforms provide the same metrics (CPC, clicks, conversions), the allocation of your budget can be elastically applied to where it provides the best return. This is aligned with optimization best practices. To identify the proper companies and job roles, you can do investigative lead research work by using tools such as Leadfeeder, LinkedIn Sales Navigator, Ghostery, and Vainu.io.

Conclusion

There are many targeting options for B2B digital marketing. For example,

  • income
  • net worth
  • IP address
  • industry
  • company
  • job title

Each has some strength and weaknesses. Ideally, the B2B digital marketing process captures the best parts of each platforms. Because we use LinkedIn, we can be sure that the seed audience is of high quality and accuracy. Then, we use the other platform’s superior reach and cheaper prices to re-address this audience with what I call ‘continued information’ (=not the same we told them already, but something more). We can also use GDN to narrow down the placement, thereby including only specific venues in retargeting.

Targeting or discovery?

Finally, I wanted to discuss an important matter. That is, some proponents of digital marketing suggest to foresake the notion of targeting altogether and focus on ‘inbound marketing’. The theory goes so that being present in social media venues where the industry folks participate, e.g. by answering their questions, one can build a reputation of opinion leader and therefore gain organic leads. Moreover, the inbound tactics entail the publication of free knowledge resources, such as ebooks, webinar and blog posts, all intended to attract organic traffic from social media and search engines to the company’s website. I’ve previously described inbound marketing as a paradigm that defines people as rational agents actively engaged in information retrieval activities, a view which contrasts seeing them as passive “targets” of advertising. Depending on which paradigm you subscribe to as a marketer, you might want to either maximize your targeting or your discoverability.

To many, it makes sense to view people as active information seekers. However, in the flipside I’ve observed inbound marketing is often uncertain and time-consuming process. In addition, companies engaged in struggle to measure their efforts effectively and actually deliver a credible ROI figure for inbound. Finally, the capabilities needed for that correspond to those needed for running a magazine publication, i.e. are not readily available in most organizations. The content game is fiercely competitive, and when you normalize the cost, e.g. CPM prices can be higher than for advertising. Or, the reach is very low, meaning that you really don’t get the impact you’re after. In my opinion, it makes no sense to foresake advertising – advertising should be the primary element in the marketing mix, and should you want to try out inbound marketing, its results should be transformed to conmensurable metrics that enable comparison between advertising and inbound marketing.

Please share if you have further ideas!

Further reading

Using Facebook Ads for B2B Targeting: http://www.practicalecommerce.com/Using-Facebook-Ads-for-B2B-Targeting

Google AdWords for B2B Organizations: 8 Questions Leadership Should Ask: https://komarketing.com/blog/google-adwords-for-b2b-organizations/

15 Audiences You Should Be Targeting with B2B Facebook Ads: https://komarketing.com/blog/15-audiences-you-should-be-targeting-with-b2b-facebook-ads/

How to achieve a good score from Google Pagespeed Insights

Speed is one of the most important ranking factors in technical search-engine optimization (SEO). Besides search rankings, pagespeed directly or indirectly influences Quality Score in AdWords (and thus to click prices), and, perhaps most importantly, to the usability of website. Better usability, more conversions.

PageSpeed Insights is a free service by Google to score the speed of a website. 100/100 is the maximum score.

WordPress, then again, is the world’s most popular content management system. According to some estimates, more than 50% of the Web is powered by WordPress. Also my blog is built on WP. I spent some time to optimize its pagespeed score, and managed to get it to pretty decent numbers (see the picture).

Fig. 1 Performance of jonisalminen.com (GTmetrix)

That is, starting from 80-something and ending up with 90-something. I’d like to have the perfecto score of 100/100, but I’ll settle for this for now since it already took many hours of work to get here. At some point when I have more time, I might continue the optimization process.

Anyway, here are some lessons I learned while making speed improvements:

  • Choose a lean theme – basically theme makes a huge difference. Re-test pagespeed with different themes, for example Schema or the one I use in my blog.
  • Use a dedicated WP hosting – I’m using GoDaddy, but e.g. WP Engine, Studiopress (Genesis Framework) and SiteGround are apparently better
  • Use Clouflare CDN (the free version) – other CDNs are probably good too, but this one is amazing
  • Load everything possible locally instead of calling externally (fonts, scripts, avatars…)
  • Use EWWW Image Optimizer (or similar, like Smush) to optimize image size; also, avoid scaling dimensions in the browser
  • Use GTMetrix (or similar) to check the waterfall of the pageload, and work back to reduce requests
  • Use WP User Avatar plugin to avoid retrieving profile pics from Gravatar
  • Use Autoptimize plugin to concatenate CSS and JS code
  • Place JavaScrip to footer to avoid render-blocking

…and, truth be told, *avoid* using plugins too many plugins. Here’s an example picture showing their impact on pageload. Plugins are easy when you can’t code or want quick fixes, but they introduce unnecessary overhead to page complexity.

Fig. 2 Impact of plugins on site performance (P3 Profiler)

In this example, plugin impact is 82.6% of page load time. In particular, I should stop using WP User Avatar, and instead change the code manually to achieve the same functionality.

Finally, here’s a list of great resources for optimizing pagespeed for WordPress sites:

Conclusion

I recommend every online marketer to spend some hands-on time with speed optimization. Gets you thinking differently about “nice layouts” and all the other marketing things. Since many websites are actually ignoring speed optimization, you can get a competitive advantage by spending time on speed optimization. And as we know, even a small edge in Google results can mean serious revenue gains because of the overwhelming search volumes.

How to do political marketing on social media? A systematic process leveraging Facebook Ads

This post very briefly explains a process of using and scaling Facebook advertising for political marketing. It might not be clear for all readers, but professional online marketers should be able to follow.

The recipe for political marketing by using Facebook Ads:

  1. Create starting parameters (Age, Gender, Location, Message)
  2. Create total combinations based on the starting parameters
  3. Use prior information to narrow down search space: e.g., identify the 100 most important target groups (e.g., battle-ground states)
  4. Create Facebook Ads campaigns based on narrowed down search space
  5. Run the campaigns (the shortest time is one day, but I would recommend at least 2-3 days to accommodate Facebook’s algorithm)
  6. Analyze the results; combine data to higher level clusters (i.e., aggregate performance stats with matching groups from different campaigns)
  7. Scale up; allocate budget based on performance, iteratively optimize for non-engaged but important groups, and remove the already-converted voters.

The intuition:

You are using Facebook Ads to test how many different target groups respond to your message. You will cluster this data to identify the most engaged target groups. You will then try to maximize voter turnout within those groups (i.e., maximize conversion). In addition, you will create new messages for those groups which are not currently responding well but which you need to capture in order to win the election. You will keep testing these groups by creating new messages, one by one finding the most responsive groups for a given message.

Once a target group shows a high level of engagement, you will scale up your advertising efforts (think 10x or 100x increase). You will keep the test cycle short (a week is more than enough), and the scaling period long. Based on campaign events, you may want to revisit already secured groups to ensure their engagement remains high. Because you are not able to measure the ultimate conversion (=voting directly), you will use proxy metrics that reflect the engagement of different target groups (particularly, clicks, CTR, post-click behavior such as time-on-site, newsletter subscriptions). This enables you to predict likelihood to vote based on social media engagement. Once a person has “converted”, he or she is removed from targeting – this is done to avoid wasting your budget by preaching to the choir.

Here are some additional metrics you can consider, some of them are harder to infer than the basic ones: frequency of activity, sentiment level, interest in a single issue that cause votes, and historical voting records (district level). According to different metrics used, we can set a target level (e.g., time-on-site > 3 mins) or binary event (subscription to campaign newsletter) which represents conversion.

Overall, we try to mimic the best practices of online marketing optimization here by 1) testing with explore-exploit mentality (scaling appropriately), and 2) excluding those who converted from future targeting (in effect, they are moved into a different budget which is direct targeting by email – a form which is more personal and cheaper than ads). In addition, we delimit the search space by using our prior information on the electorate, again to avoid wasteful impressions and maximize ROI-efficiency.

Then, we fill the selected groups with data and observe the performance metrics. Finally, we cluster the results to get a higher-level understanding of each group, as well as find points of agreement between the groups that can be used to refine the communication strategy of the larger political campaign. Therefore, the data we obtain is not solely limited to Facebook Ads but can be used to further enhance messaging in other channels as well.

There. The methodology represents a systematic and effective way to leverage Facebook Ads for political social media marketing.

Also read:

Agile methods for predicting contest outcomes by social media analysis

Analyzing sentiment of topical dimensions in social media

Affinity analysis in political social media marketing – the missing link

Affinity analysis in political social media marketing – the missing link

Introduction. Hm… I’ve figured out how to execute successful political marketing campaign on social media [1], but one link is missing still. Namely, applying affinity analysis (cf. market basket analysis).

Discounting conversions. Now, you are supposed to measure “conversions” by some proxy – e.g., time spent on site, number of pages visited, email subscription. Determining which measurable action is the best proxy for likelihood of voting is a crucial sub-problem, which you can approach with several tactics. For example, you can use the closest action to final conversion (vote), i.e. micro-conversion. This requires you have an understanding of the sequence of actions leading to final conversion. You could also use a relative cut-off point; e.g. the nth percentile with the highest degree of engagement is considered as converted.

Anyhow, this is very important because once you have secured a vote, you don’t want to waste your marketing budget by showing ads to people who already have decided to vote for your candidate. Otherwise, you risk “preaching to the choir”. Instead, you want to convert as many uncertain voters to voters as possible, by using different persuasion tactics.

Affinity analysis. The affinity analysis can be used to accomplish this. In ecommerce, you would use it as a basis for recommendation engine for cross-selling or up-selling (“customers who bought this item also bought…” à la Amazon). First you detemine which sets of products are most popular, and then show those combinations to buyers interested in any item belonging to that set.

In political marketing, affinity analysis means that because a voter is interested in topic A, he’s also interested in topic B. Therefore, we will show him information on topic B, given our extant knowledge his interests, in order to increase likelihood of conversion. This is a form of associative

Operationalization. But operationalizing this is where I’m still in doubt. One solution could be building an association matrix based on website behavior, and then form corresponding retargeting audiences (e.g., website custom audiences on Facebook). The following picture illustrates the idea.

Figure 1 Example of affinity analysis (1=Visited page, 0=Did not visit page)

For example, we can see that themes C&D and A&F commonly occur together, i.e. people visit those sub-pages in the campaign site. You can validate this by calculating correlations between all pairs. When you set your data in binary format (0/1), you can use Pearson correlation for the calculations.

Facebook targeting. Knowing this information, we can build target audiences on Facebook, e.g. “Visited /Theme_A; NOT /Theme_F; NOT /confirmation”, where confirmation indicates conversion. Then, we would show ads on Theme F to that particular audience. In practice, we could facilitate the process by first identifying the most popular themes, and then finding the associated themes. Once the user has been exposed to a given theme, and did not convert, he needs to be exposed to another theme (with the highest association score). The process is continued until themes run out, or the user converts, which ever comes first. Applying the earlier logic of determining proxy for conversion, visiting all theme sub-pages can also be used as a measure for conversion.

Finally, it is possible to use more advanced methods of associative learning. That is, we could determine that {Theme A, Theme F} => {Theme C}, so that themes A and B predict interest in theme C. However, it is more appropriate to predict conversion rather than interest in other themes, because ultimately we’re interested in persuading more voters.

Footnotes

[1] Posts in Finnish:

https://www.facebook.com/joni.salminen.33/posts/10212240031455606

https://www.facebook.com/joni.salminen.33/posts/10212237230465583

Total remarketing – the concept

Here’s a definition:

Total remarketing is remarketing in all possible channels with all possible list combinations.

Channels:

  • Programmatic display networks (e.g., Adroll)
  • Google (GDN, RLSA)
  • Facebook (Website Custom Audience)
  • Facebook (Video viewers / Engaged with ads)
  • etc.

How to apply:

  1. Test 2-3 different value propositions per group
  2. Prefer up-selling and cross-selling over discounts (the goal is to increase AOV, not reduce it; e.g. you can include an $20 gift voucher when basket size exceeds $100)
  3. Configure well; exclude those who bought; use information you have to improve remarketing focus (e.g. time of site, products or categories visited — the same remarketing for all groups is like the same marketing for all groups)
  4. Consider automation options (dynamic retargeting; behavior based campaign suggestions for the target)