Archive for the digital marketing tag

Joni

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

english

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/

Joni

How to achieve a good score from Google Pagespeed Insights

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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.

Joni

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

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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

Joni

Affinity analysis in political social media marketing – the missing link

english

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

Joni

Total remarketing – the concept

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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)
Joni

In 2016, Facebook bypassed Google in ads. Here’s why.

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In 2016, Facebook bypassed Google in ads. Here’s why.

Introduction

The gone 2016 was the first year I thought Facebook ends up beating Google in the ad race, despite the fact Google still dominates in revenue ($67Bn vs. $17Bn in 2015). I’ll explain why.

First, consider that Google’s growth is restricted by three things:

  1. natural demand
  2. keyword volumes, and
  3. approach of perfect market.

More demand than supply

First, at any given time there is a limited number of people interested in a product/service. The interest can be of purchase intent or just general interest, but either way it translates into searches. Each search is an impression that Google can sell to advertisers through its AdWords bidding. The major problem is this: even when I’d like to spend more money on AdWords, I cannot. There is simply not enough search volume to satisfy my budget (in many cases there is, but in highly targeted and profitable campaigns many times there isn’t). So, the excess budget I will spend elsewhere where the profitable ad inventory is not limited (that is, Facebook at the moment).

Limited growth

According to estimates, search volume is growing by 10-15% annually [1]. Yet, Google’s revenue is expected to grow even by 26% [2]. Over the year, Google’s growth rate in terms of search volume has substantially decreased, although this is perceived as a natural phenomenon (after trillion searches it’s hard to keep growing double digits). In any case, the aforementioned dynamics reflect to search volumes – when the volumes don’t grow much and new advertisers keep entering the ad auction, there is more competition over the same searches. In other words, supply stays stable but demand increases, resulting in more intense bid wars.

Approaching perfect market

For a long time now, I’ve added +15% increase in internal budgeting for AdWords, and last year that was hard to maintain. Google is still a profitable channel, but the advertisers’ surplus is decreasing year by year, incentivizing them to look for alternative channels. While Google is restrained by its natural search volumes, Facebook’s ad inventory (=impressions) are practically limitless. The closer AdWords gets to a perfect market (=no economic rents), the less attractive it is for savvy marketers. Facebook is less exploited, and allows rents.

What will Google do?

Finally, I don’t like the Alphabet business. Already in the beginning it signals to investors that Google is in “whatever comes to mind” business instead of strategic focus on search. Most likely Alphabet ends up draining resources from the mother company, producing loss and taking human capital off from succeeding in online ads business (which is where their money comes from). In contrast, Facebook is very focused on social; it buys off competitors and improves fast. That said, I do have to recognize that Google’s advertising system is still much better than that of Facebook, and in fact still the best in the world. But momentum seems to be shifting to Facebook’s side.

Conclusion

The maximum number of impressions (=ad inventory) of Facebook is much higher than that of Google, because Google is limited by natural demand and Facebook is not. In the marketplace, there is always more supply than demand which is why advertisers want to spend more than what Google enables. These factors combined with Facebook’s continously increasing ability to match interested people with the right type of ads, makes Facebook’s revenue potential much bigger than Google’s.

From advertiser’s perspective, Facebook and Google both are and are not competitors. They are competitors for ad revenue, but they are not competitors in the online channel mix. Because Google is for demand capture and Facebook for demand creation, most marketers want to include both in their channel mix. This means Google’s share of online ad revenue might decrease, but a rational online advertisers will not drop its use so it will remain as a (less important) channel into foreseeable future.

References

[1] http://www.internetlivestats.com/google-search-statistics/

[2] http://venturebeat.com/2016/09/27/4-graphs-show-the-state-of-facebook-and-googles-revenue-dominance/

Joni

Defining SMQs: Strategic Marketing Questions

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Introduction

Too often, marketing is thought of being advertising and nothing more. However, already Levitt (1960) and Kotler (1970) established that marketing is a strategic priority. Many organizations, perhaps due to lack of marketers in their executive boards, have since forgotten this imperative.

Another reason for decreased importance of marketing is due to marketing scholars pushing the idea that “everything is marketing” which leads to decay of the marketing concept – if it is everything, it is nothing.

Nevertheless, if we reject the omni-marketing concept and return to the useful way of perceiving marketing, we observe the linkage between marketing and strategy.

Basic questions

Tania Fowler wrote a great piece on marketing, citing some ideas of Professor Roger Martin’s HBR article (2014). Drawing from that article, the basic strategic marketing questions are:

  • Who are our customers? (segmentation)
  • Why do they care about our product? (USPs/value propositions/benefits)
  • How are their needs and desires evolving? (predictive insight)
  • What potential customers exist and why aren’t we reaching them? (market potential)

This is a good start, but we need to expand the list of questions. Borrowing from Osterwalder (2009) and McCarthy (1960), let’s apply BMC (9 dimensions of a business model) and 4P marketing mix thinking (Product, Place, Promotion, Price).

Business Model Canvas approach

This leads to the following set of questions:

  • What is the problem we are solving?
  • What are our current revenue models? (monetization)
  • How good are they from customer perspective? (consumer behavior)
  • What is our current pricing strategy? (Kotler’s pricing strategies)
  • How suitable is our pricing to customers? (compared to perceived value)
  • How profitable is our current pricing?
  • How competitive is our current pricing?
  • How could our pricing be improved?
  • Where are we distributing the product/solution?
  • Is this where customers buy similar products/solutions?
  • What are our potential revenue models?
  • Who are our potential partners? Why? (nature of win-win)

Basically, each question can be presented as a question of “now” and “future”, whereupon we can identify strategic gaps. Strategy is a lot about seeing one step ahead — the thing is, foresight should be based on some kind of realism, or else fallacies take the place of rationality. Another point from marketing and startup literature is that people are not buying products, but solutions (solution-based selling, product-market fit, etc.) Someone said the same thing about brands, but I think solution is more accurate in the strategic context.

Adding competitors and positioning

The major downside of BMC and 4P thinking from strategic perspective is their oversight of competition. Therefore, borrowing from Ries and Trout (1972) and Porter (1980), we add these questions:

  • Who are our direct competitors? (substitutes)
  • Who are our indirect competitors? (cross-verticality, e.g. Google challenging media companies)
  • How are we different from competitors? (value proposition matrix)
  • Do our differentiating factors truly matter to the customers? (reality check)
  • How do we communicate our main benefits to customers? (message)
  • How is our brand positioned in the minds of the customers? (positioning)
  • Are there other products customers need to solve their problem? What are they? (complements)

Defining the competitive advantage, or critical success factors (CSFs), leads into natural linkage to resources, as we need to ask what are the resources we need to execute, and how to acquire and commit those resources (often human capital).

Resource-based view

Therefore, I’m turning to resource-based thinking in asking:

  • What are our current resources?
  • What are the resources we need to be competitive? (VRIN framework)
  • How to we acquire those resources? (recruiting, M&As)
  • How do we commit those resources? (leadership, company culture)

Indeed, company culture is a strategic imperative which is often ignored in strategic decision making. Nowadays, perhaps more than ever, great companies are built on talent and competence. Related strategic management literature deals with dynamic capabilities (e.g., Teece, 2007) and resource-based view (RBV) (e.g., Wernerfelt, 1984). In practice, companies like Facebook and Google do everything possible to attract and retain the brightest minds.

Do not forget profitability

Finally, even the dreaded advertising questions have a strategic nature, relating to customer acquisition and loyalty, as well as ROI in regards to both as well as to our offering. Considering this, we add:

  • How much does it cost to acquire a new customer?
  • What are the best channels to acquire new customers?
  • Given the customer acquisition cost (CAC) and customer lifetime value (CLV), are we profitable?
  • How profitable are each products/product categories? (BCG matrix)
  • How can we make customers repeat purchases? (cross-selling, upselling)
  • What are the best channels to encourage repeat purchase?
  • How do we encourage customer loyalty?

As you can see, these questions are of strategic nature, too, because they are directly linked to revenue and customer. After all, business is about creating customers, as stated by Peter Drucker. However, Drucker also maintained that a business with no repeat customers is no business at all. Thus, marketing often focuses on customer acquisition and loyalty.

The full list of strategic marketing questions

Here are the questions in one list:

  1. Who are our customers? (segmentation)
  2. Why do they care about our product? (USPs/value propositions/benefits)
  3. How are their needs and desires evolving? (predictive insight)
  4. What potential customers exist and why aren’t we reaching them? (market potential)
  5. What is the problem we are solving?
  6. What are our current revenue models? (monetization)
  7. How good are they from customer perspective? (consumer behavior)
  8. What is our current pricing strategy? (Kotler’s pricing strategies)
  9. How suitable is our pricing to customers? (compared to perceived value)
  10. How profitable is our current pricing?
  11. How competitive is our current pricing?
  12. How could our pricing be improved?
  13. Where are we distributing the product/solution?
  14. Is this where customers buy similar products/solutions?
  15. What are our potential revenue models?
  16. Who are our potential partners? Why? (nature of win-win)
  17. Who are our direct competitors? (substitutes)
  18. Who are our indirect competitors? (cross-verticality, e.g. Google challenging media companies)
  19. How are we different from competitors? (value proposition matrix)
  20. Do our differentiating factors truly matter to the customers? (reality check)
  21. How do we communicate our main benefits to customers? (message)
  22. How is our brand positioned in the minds of the customers? (positioning)
  23. Are there other products customers need to solve their problem? What are they? (complements)
  24. What are our current resources?
  25. What are the resources we need to be competitive? (VRIN framework)
  26. How to we acquire those resources? (recruiting, M&As)
  27. How do we commit those resources? (leadership, company culture)
  28. How much does it cost to acquire a new customer?
  29. What are the best channels to acquire new customers?
  30. Given the customer acquisition cost (CAC) and customer lifetime value (CLV), are we profitable?
  31. How profitable are each products/product categories? (BCG matrix)
  32. How can we make customers repeat purchases? (cross-selling, upselling)
  33. What are the best channels to encourage repeat purchase?
  34. How do we encourage customer loyalty?

The list should be universally applicable to all companies. But filling in the list is not “oh, let me guess” type of exercise. As you can see, answering to many questions requires customer and competitor insight that, as the startup guru Steve Blank says, needs to be retrieved by getting out of the building. Those activities are time-consuming and costly. But only if the base information is accurate, strategic planning serves a purpose. So don’t fall prey to guesswork fallacy.

Implementing the list

One of the most important things in strategic planning is iteration — it’s not “set and forget”, but “rinse and repeat”. So, asking these questions should be repeated from time to time. However, people tend to forget repetition. That’s why corporations often use consultants — they need fresh eyes to spot opportunities they’re missing due to organizational myopia.

Moreover, communicating the answers across the organization is crucial. Having a shared vision ensures each atomic decision maker is able to act in the best possible way, enabling adaptive or emergent strategy as opposed to planned strategy (Mintzberg, 1978). For this to truly work, customer insight needs to be internalized by everyone in the organization. In other words, strategic information needs to be made transparent (which it is not, in most organizations).

And for the information to translate into action, the organization should be built to be nimble; empowering people, distributing power and reducing unnecessary hierarchy. People are not stupid: give them a vision and your trust, and they will work for a common cause. Keep them in silos and treat them as sub-ordinates, and they become passive employees instead of psychological owners.

Concluding remarks

We can say that marketing is a strategic priority, or that strategic planning depends on the marketing function. Either way, marketing questions are strategic questions. In fact, strategic management and strategic marketing are highly overlapping concepts. Considering both research and practice, their division can be seen artificial and even counter-productive. For example, strategic management scholars and marketing scholars may speak of the same things with different names. The same applies to the relationship between CEOs and marketing executives. Joining forces reduces redundancy and leads to a better future of strategic decision-making.

Joni

Meaningless marketing

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Meaningless marketing

I’d say 70% of marketing campaigns have little to no real effect. Most certainly they don’t have a positive return in hard currency.

Yet, most marketers spend their time running around, planning all sorts of campaigns and competitions people couldn’t care less of. They are professional producers of spam, where in fact they should be focusing on core of the business: understanding why customers buy, how could they buy more, what sort of products should we make, how can the business model be improved, etc. The wider concept of marketing deals with navigating the current and the future market; it is not about making people buy stuff they don’t need.

To a great extent, I blame the marketing education. In the academia, we don’t really get the real concept of marketing into our students’ minds. Even the students majoring in marketing don’t truly “get” that marketing is not the same as advertising; too often, they have a narrow understanding of it and are then easily molded into the perverse industry standards, ending up in the purgatory of meaningless campaigns while convincing themselves they’re doing something of real value.

But marketing is not about campaigns, and it sure as hell is not about “creating Facebook competitions”. Rather, marketing is a process of continuous improvement of the business. Yes, this includes campaigns because the business cycles in many industries follow seasonal patterns, and we need to communicate outwards. But marketing has so much more to give for strategy, if only marketers would stop wasting their time and instead focus on the essential.

Now, what I wrote here is only based on anecdotal evidence arising from personal observations. It would be interesting, and indeed of great importance, to find out if it’s correct that most marketers are wasting their time on petty campaigns instead of the big picture. This could be done for example by conducting a study that answers the questions:

  1. What do marketers do with their time?
  2. How does that contribute to the bottom line?
  3. Why? (That is, what is the real value created for a) the customer and b) the organization)
  4. How is the value being measured and defended inside the organization?

If nothing else, every marketer should ask themselves those questions.

Joni

Facebook Ads: remember data breakdowns

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Here’s a small case study.

We observed irrational behavior from Facebook ads. We have two ad versions running; but the one with lower CTR gets a better relevance score and lower CPC.

This seems like an irrational outcome, because in my understanding, CTR as a measure of relevance should be largest impact factor to CPC and Relevance Score.

Figure 1  Aggregate data

So, we dug a little bit futher and did a breakdown of the data. It turns out, the ad version with lower aggregate CTR performs better on mobile. Apparently this adds emphasis to the algorithm’s calculation.

Figure 2  Breakdown data

Lesson learned: Always dig in deeper to understand aggregate numbers. (If you’re interested in learning more about aggregate data problems, do a lookup on “Simpson’s paradox”.)

Joni

What is a “neutral algorithm”?

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What is a “neutral algorithm”?

1. Introduction

Earlier today, I had a brief exchange of tweets with @jonathanstray about algorithms.

It started from his tweet:

Perhaps the biggest technical problem in making fair algorithms is this: if they are designed to learn what humans do, they will.

To which I replied:

Yes, and that’s why learning is not the way to go. “Fair” should not be goal, is inherently subjective. “Objective” is better

Then he wrote:

lots of things that are really important to society are in no way objective, though. Really the only exception is prediction.

And I wrote:

True, but I think algorithms should be as neutral (objective) as possible. They should be decision aids for humans.

And he answered:

what does “neutral” mean though?

After which I decided to write a post about it, since the idea is challenging to explain in 140 characters.

2. Definition

So, what is a neutral algorithm? I would define it like this:

“A neutral algorithm is a decision-making program whose operating principles are minimally inflenced by values or opinions of its creators.” [1]

An example of a neutral algorithm is a standard ad optimization algorithm: it gets to decide whether to show Ad1, Ad2, or Ad3. As opposed to asking from designers or corporate management which ad to display, it makes the decision based on objective measures, such as click-through rate (CTR).

A treatment that all ads (read: content, users) get is fair – they are diffused based on their merits (measured objectively by an unambiguous metric), not based on favoritism of any sort.

3. Foundations

The roots of algorithm neutrality stem from freedom of speech and net neutrality [2]. No outsiders can impose their values and opinions (e.g., censoring politically sensitive content) and interfere with the operating principles of the algorithm. Instead of being influenced by external manipulation, the decision making of the algorithm is as value-free (neutral) as possible. For example, in the case of social media, it chooses to display information which accurately reflects the sentiment and opinions of the people at a particular point in time.

4. Limitations

Now, I grant there are issues with “freedom”, some of which are considerable. For example, 1) for media, CTR-incentives lead to clickbaiting (alternative goal metrics should be considered), 2) for politicians and electorate, facts can be overshadowed by misinformation and short videos taken out of context to give false impression of individuals; and 3) for regular users, harmful misinformation can spread as a consequnce of neutrality (e.g., anti vaccination propaganda).

Another limitation is legislation – illegal content should be kept out by the algorithm. In this sense, the neutral algorithm needs to adhere to a larger institutional and regulatory context, but given that the laws themselves are “fair” this should impose no fundamental threat to the objective of neutral algorithms: free decision-making and, consequently, freedom of speech.

I wrote more about these issues here [3].

5. Conclusion

Inspite of the aforementioned issues, with a neutral algorithm each media/candidate/user has a level playing field. In time, they must learn to use it to argue in a way that merits the diffusion of their message.

The rest is up to humans – educated people respond to smart content, whereas ignorant people respond to and spread non-sense. A neutral algorithm cannot influence this; it can only honestly display what the state of ignorance/sophistication is in a society. A good example is Microsoft’s infamous bot Tay [4], a machine learning experiment turned bad. The alarming thing about the bot is not that “machines are evil”, but that *humans are evil*; the machine merely reflects that. Hence my original point of curbing human evilness by keeping algorithms free of human values as much as possible.

Perhaps in the future an algorithm could figuratively spoken save us from ourselves, but at the moment that act requires conscious effort from us humans. We need to make critical decisions based on our own judgment, instead of outsourcing ethically difficult choices to algorithms. Just as there is separation of church and state, there should be separation of humans and algorithms to the greatest possible extent.

Notes

[1] Initially, I thought about definition that would say “not influenced”, but it is not safe to assume that the subjectivity of its creators
would not in some way be reflected to the algorithm. But “minimal” leads into normative argument that that subjectivity should be mitigated.

[2] Wikipedia (2016): “Net neutrality (…) is the principle that Internet service providers and governments should treat all data on the Internet the same, not discriminating or charging differentially by user, content, site, platform, application, type of attached equipment, or mode of communication.”

[3] Algorithm Neutrality and Bias: How Much Control? <https://www.linkedin.com/pulse/algorithm-neutrality-bias-how-much-control-joni-salminen>

[4] A part of the story is that Tay was trolled heavily and therefore assumed a derogatory way of speech.