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Argument: Personas lose to ‘audience of one’

Introduction. In this post, I’m exploring the usefulness of personas in digital analytics. At Qatar Computing Research Institute (QCRI), we have developed a system for automatic persona generation (APG) – see the demo. Under the leadership of Professor Jim Jansen, we’re constantly working to position this system toward the intersection of customer profiles, personas, and analytics.

Three levels of data. Imagine three levels of data:

  •  customer profiles (individual)
  • personas (aggregated individual)
  • statistics (aggregated numbers: tables and charts)

Which one is the best? The answer: it depends.

Case of advertising. For advertising, usually the more individual the data, the better. The worst case is the mass advertising, where there is one message for everyone: it fails to capture the variation of preferences and tastes of the underlying audience, and is therefore inefficient and expensive. Group-based targeting, i.e. market segmentation (“women 25-34”) performs better because it is aligning the product features with the audience features. Here, the communalities of the target group allow marketers to create more tailored and effective messages, which results in less wasted ad impressions.

Case of design and development. In a similar vein, design is moving towards experimentation. You have certain conventions, first of all, that are adopted industry-wide in the long run (e.g., Amazon adopts a practice and small e-commerce sites follow suit). Many prefer being followers, and it works for the most part. But multivariate testing etc. can reveal optimal designs better than “imagining a user” or simply following conventions. Of course, personas, just like any other immersive technique, can be used as a source of inspiration and ideas. But they are just one technique, not the technique.

For example, in the case of mobile startups I would recommend experimentation over personas. A classic example is Instagram that found from data that filters were a killer feature. For such applications, it makes sense to define an experimental feature set, and adjust if based on behavioral feedback from the users.

Unfortunately, startup founders often ignore systematic testing because they have a pre-defined idea of the user (à la persona) and are not ready to get their ideas challenged. The more work is done to satisfy the imaginary user, the more harder it becomes to make out-of-the-box design choices. Yet, those kind of changes are required to improve not by small margins but by orders of magnitude. Eric Ries call this ‘sunk code fallacy’.

In my opinion, two symptoms predating such a condition can be seen when the features are not

  1. connected to analytics, so that tracking of contribution of each is possible (in isolation & to the whole)
  2. iteratively analyzed with goal metrics, so that there is an ‘action->response->action’ feedback loop.

In contrast, iterative (=repetitive) analysis of performance of each feature is a modern way to design mobile apps and websites. Avoiding the two symptoms is required for systematic optimization. Moreover, testing the features does not need to take place in parallel, but it can be one by one as sequential testing. This can in fact be preferable to avoid ‘feature creep’ (clutter) that hinders the user experience. However, for sequential testing it is preferable to create a testing roadmap with a clear schedule – otherwise, it is too easy to forget about testing.

Strategic use cases show promise. So, what is left for personas? In the end, I would say strategic decision making is very promising. Tactical and operational tasks are often better achieved by using either completely individual or completely aggregated data. But individual data is practically useless at strategic decision making. Aggregated data is useful, e.g. sales by region or customer segment, and it is hard to see anything replace that. However, personas are in between the two – they can provide more understanding on the needs and wants of the market, and act as anchor points for decision making.

Strategic decision aid is also a lucrative space; companies care less about the cost, because the decisions they make are of great importance. To correctly steer the ship, executives need need accurate information about customer preferences and have clear anchor points to align their strategic decision with (see the HubSpot case study).

In addition, aggregated analytics systems have one key weakness. They cannot describe the users very well. Numbers do not include information such as psychographics or needs, because they need to be interpreted from the data. Customer profiles are a different thing — in CRM systems, enrichment might be available but again the number of individual profiles is prohibitive for efficient decision making.

Conclusion. The more we are moving towards real-time optimization, the less useful a priori conceptualizations like target groups and personas become for marketing and design. However, they are likely to remain useful for strategic decision making and as “aggregated people analytics” that combine the coverage of numbers and the details of customer profiles. The question is: can we build personas that include the information of customer profiles, while retaining the efficiency of using large numbers? At QCRI, we’re working everyday toward that goal.

First experiences with startup user studies (ca. 2010)

I was reading through old emails while backing up my Gmail inbox with Gmvault, an open source command-line tool. Among other interesting trips to memory lane, one message was about the first startup user study I was involved with. It was for a life-coaching startup that eventually failed. In retrospect, it’s interesting to reflect on what went wrong and how we could have improved. In that spirit, I’m sharing these notes on the user study.

***

Okay, here’s my analysis based on user feedback.

We have three big issues to tackle at the moment:

  1. value
  2. mobilizing users
  3. clarity and purpose of the service

Value refers to a) providing such benefits customers want to pay for and b) setting the right price point.

Mobilizing refers to practical issue of getting customers to use the service on daily basis – i.e. facilitating the data input process as much as possible.

Clarity refers to layout and functionality of the site.

1. VALUE

a) Problem:

Are users willing to pay for the service?

“Sivusto ei tuonut mitään uutta tässä vaiheessa muihin vastaavanlaisiin sivustoihin verrattuna, paitsi jos sivustonne pysyy ilmaisena.” (“The site doesn’t bring anything new compared to other similar sites, except if it stays free.”)

-> There’s a need to introduce features people are willing to pay for.

I’ll doubt they’d pay at the moment, although we should have asked that in the feedback form.

Solutions:

-> Creating a mobile version for paying customers (cf. Spotify)

“Ehdottomasti mobiiliversiota tarvitaan.” (“A mobile version is absolutely needed.”)

I agree that an optimized version for mobile phones is needed. Technically this would require mobile device detection and loading appropriate html+css files to fit the smaller resolution. Later on, if the service succeeds, an iPhone/Android app would be great 🙂

-> Other additional features for paying users such as meal (1) and training recommendations (2) (integrated in the app)

(1) “You could include an suggestion for the diet. You now have something like 50% proteins, 30% Carbo., 20% fat, but would be great to see already a diet suggestion based on that.

“It could include in the categories some traditional dishes like: Fish and potatos, Pasta and tomato, Kebak, etc…”

This was a constant concern by many respondents. As Valtteri suggested earlier, building meals is a great way of providing value. We could for example divide them to three categories based on nutritional values: snacks (välipalat), light meal (kevyt ateria) and heavy meal (raskas ateria). By combining these three, the service could propose a personalised diet based on consumption and need of calories calculated from person’s weight and other factors.

Also users could be given the possibility to add own meals and make them public for other users, as suggested in this comment:

“Yksittäisten ruokalajien sijaan voisi lisätä malliannoksia ja tarvittaessa muokata niitä samalla tavalla, miten annos pitää nyt koota täysin itse.”

(2) Another idea is creating a mentoring/peer supporting system that enables users to get support

b) Problem: “How much you’re thinking to charge the subscription?”

Solutions:

  1. Yearly fee of 25€
  2. Monthly fee of 2.95€ (“less than coffee cup in Starbucks” 🙂

Free of charge -> revenue coming from advertisements/affiliate marketing

So, I think we’ll need to make a decision now of whether to offer the service for free and acquire sponsors, or make it paid and introduce such features that would increase likelihood of paying.

Also, we can think of providing a free version for free and premium with extra features for a small fee.

2. MOBILIZING USERS

Problem: Users are too lazy/busy to add daily information, ergo the app is not used actively.

“In general I think that it is really great, but the problem is to add the information on a daily basis…. :(“

“Jos palvelua alkaa käyttää niin on riskinä, että sitä vain kokeilee, eikä käytä jatkuvasti”

Solutions:

-> Creating a mobile version that allows on-the-go editing of profile

-> Creating a scheduled email system that allows easy updating (“Did you do the assigned training? Answer ‘yes’ to this message and information is updated automatically to your Muscler profile”)

-> Making it possible to update via sms? (requires an sms gateway + might be complex to use)

-> Creating community pressure for using the service (assigning personal mentors who have access to a person’s progress data — while conserving anonymity)

-> Offering rewards after completing milestones (e.g. reductions of sponsor products such as proteins)

3. CLARITY AND PURPOSE

“On vähän epäselvä. Ohjeet voisi olla vaikka tyyliä ‘ranskalaiset viivat’ settiä” (“A bit unclear. Bulleted instructions needed”)

“Parantakaa/tiivistäkää putkea, joka alkaa tarvoitteiden asetannasta ja etenee seurannan aloittamiseen ja raportteihin, tällä hetkellä minun tulee itse välillä muistaa edetä seuraavaan kohtaan ilman kehhoitusta/vinkkiä” (“Improve the process starting from setting goals to tracking and reports – atm, i have to remember to move myself”)

“Tutoriaali tai step-by-step-ohjeet olisivat hyvät :)” (“tutorial or step-by-step instructions would be great”)

-> Inserting tooltips

-> Creating “Proceed to [next]” buttons (Proceed to Tracking/Reports)

-> Creating a “How to use?” page

Besides the video, a separate “How to use?” page is needed. a bullet-type list would do ok, like suggested by the user. it could contain steps of using the service along with links, being short and simple.

“Mitä tarkoittaa tarkalleen Vahvistus tai ‘Lisää kestävyyttä’. Näistä olisi hyvä olla tietoa, sillä eri lajien ihmiset saattavat nähdä nuo eri tavoin.”

(“What does strenghten or more endurance mean?”)

-> Maybe these could be removed and focus on mass increase?

4. Bugs & Language

“Suosittelisitko palvelua ystävillesi?” kohdassa sanasta “Kyllä” puuttuu toinen l-kirjain (“Kylä”). :)”

-> Kylä should be Kyllä on feedback page

“Tavoite laatikon perässä saisi olla mitä yksikköä siinä halutaan”

-> Add units after goal form field to clarify what is asked.

“Kun lisäsin ruokia omalle “tililleni”, ei missään tuntunut olevan hiilihydraatteja. Se valikko, mistä ruuat valitaan, voisi olla selkeämpi (esim. isompi ja ADD-nappula aina samassa kohtaa). Haku-toiminto ruoka-aineille olisi myös kätevä.”

-> search function for nutrition, bigger add button with fixed position

“Itse tykkään etsiä aina lisätietoja varsinkin tällaisista urheiluun liittyvistä aiheista, joista löytyy aina monia mielipiteitä. Siksi olisin kaivannut esim. linkkejä lisätietoon “The Harris Benedict Equation” -menetelmästä”

-> Insert a link to “The Harris Benedict Equation”

“-Report is showing for today: 364% ACHIEVED (!!??)”

This is an issue I spotted myself, too. There must be something bizarre in the calculation formula, or can you give me example of a proper diet of 100% per day for let’s say a person weighing 60kg?

“- Many times a orange button option is dimmed, and little confusing (do I need to enter more infp? Does it work?)”

-> The button should be yellow in normal state and changed in mouseover, not other way around.

In reports/diet, “viikottain” should be “viikoittain”

Traning advice -> Training advice

Also, server location still shows Ukraine. There’s still some timelag which can be annoying.

There’s a logic problem -> when i insert a weightlifting training of 5 x 35kg in goals and then go into tracking, i shouldn’t reinsert the same stuff. i should be able to select the activity and then check that it’s done. if the weight was different, it should be changed in goals and not here.

***

Social media marketing for researchers: How to promote your publications and reach the right people

Today the Social Computing group at Qatar Computing Research Institute had the pleasure of listening to the presentation of Luis Fernandez Luque about social media marketing for researchers. Luis talked about how to promote your publications and personal brand, as well as how to reach the right people on social media with your research.

Luis is one of the most talented researchers I know, and a very good friend. He has two amazing girls and a great wife. You can follow Luis’ research on health informatics on Slideshare, Twitter, and of course connect with him on LinkedIn.

In this post, I’ll summarize some points of his presentation (if you want the full thing, you need to ask him :), and reflect them on my own experiences as a digital marketer.

Without further ado, here are 7 social media tips for researchers.

1. Upload your articles to the 3 big social media platforms for researchers

According to Luis, there are three major social media sites for researchers. These are:

You should post your papers on each of these platforms to get extra visibility. According to Luis, the point is to disseminate content in existing platforms because they have the critical mass of audience readily available. This is preferable to starting your own website from scratch and trying to attract visitors.

However, I recommend doing both. In addition to sharing your research on social media, you can have a separate websites for yourself and dedicated websites for your research projects. Having dedicated websites with a relevant domain provides search-engine optimization (SEO) benefits. In particular, websites are indexed better than social media sites which means you have a better chance of being found. Your papers will get indexed by search engines and therefore will attract occasional hits, depending on your chosen keywords and competition for them (see point number 4).

For the same reason you want to effectively cross-link and cross-post your content. For example, 1) publish the post in your own website, 2) re-publish it on LinkedIn, and 3) share on Twitter, LinkedIn, and Google+ (as well as researcher social networks, if it’s academic content, but here I’m referring to idea posts or popularized articles). Don’t forget Google+, because occasionally those posts show up in search results. Sharing can be repeated and schedule by using BufferApp. For example, I have all my LinkedIn articles mirrored at jonisalminen.com.

Finally, besides your research papers, consider sharing your dissertation as well as Bachelor/Master theses. Those are often easier to read and reach a wider audience.

2. Recycle content and ideas

Luis mentioned he was able to increase the popularity of one of his papers by creating a Slideshare presentation about it. This principle is more commonly known as content tree in inbound marketing. I completely agree with Luis’ advice – it is often straight-forward and fast to create a presentation based on your existing paper, because you already know what you want to say.

If you have conference presentations or teaching material readily available, even better. For example, I’ve shared all my digital marketing lectures and teaching material at Slideshare, and they steadily attract views (tens of thousands in total so far). Here is an example of a presentation I made based on the post you’re reading. As you can see, it has an interesting title that aims to be “search-engine optimized”. By scrolling down, you also notice that Slideshare converts the presentation also into pure text. This is good for search-engine visibility, and one reason why Slideshare presentations rank well in Google. The picture from my Slideshare Analytics shows many people find the presentations through Google.

Figure 1 Slideshare Analytics showing large share of search traffic.

Luis also mentioned including the name of your publication in the title slide which is a good idea if you want to catch more citations from interested readers.

3. Create an online course

MOOCs and other forms of online education form a great way for disseminating your ideas and making your research more well known. Luis mentioned two platforms for this:

The point is to share knowledge and at the same time mention your own research. I think Luis mentioned he had at some point 4,000 participants for his course which is a very large audience and shows the power of online courses compared to traditional classrooms (I think I had maximum 100 students in my course, so you can see how big the difference in reach is).

4. Choose the right title

This is like copywriting for researchers. The title plays an important role for two reasons: 1) it determines whether people become interested and click forward to reading your paper, and 2) it can increase or decrease your chances of being found in Google. A straight analogy is journalism: you want some degree of click-bait in your title, because you are competing against all other papers for attention. However, in my experience many scholars pay little attention to the attractiveness of the title of their paper from the clicker’s perspective, and even fewer perform keyword research (the post in Finnish) to find out about popularity of related keywords.

So, how to choose the title of a research paper?

  1. Research & include relevant keywords
  2. Mention the problem your research deals with

The title should be catchy (=attractive) and include keywords people are using when they are searching information on the topic, be it research papers or just general knowledge. Luis’ tip was to include the problem (e.g., diabetes) in the title to get more downloads. Moreover, when sharing your papers, use relevant hashtags. In the academia, the natural way is to identify conference hashtags relating to your topic — as long as it’s relevant, using conference hashtags to promote your research is okay.

You can use tools such as Google Keyword Planner and Google Trends for keyword research. To research hashtags, Twitter’s recommendation feature is an easy approach (e.g., in TweetDeck you get recommendations when you start writing a hashtag). You can also use tools such as Hashtagify and Keyhole to research relevant hashtags. Finally, also include the proper keywords in your abstract. While full papers are often hidden behind gateways, abstracts are indexed by search engines.

5. Write guest blogs

Instead of trying to make a go with your own website (which is admittedly tough!), Luis recommended to write guest posts in a popular blogs. The rationale is the same as in the case of social media platforms: these venues already have an audience. As long as the blog deals with your vertical, the audience is likely to be interested in what you say. For content marketers, getting quality content is also a consistent source of concern, so it is easy to see a win-win here.

For example, you can write to research foundation blog. In case they gave you money, this also serves to show you are actively trying to popularize your research, and they get something in return for their money! Consider also industry associations (e.g., I haven’t come around to it yet, but I would like to write to IAB Finland’s blog since they have a large audience interested in digital marketing).

6. Define your audience

Luis advised to define your audience carefully – it is all about determining your area of focus and where you want to make an impact. On social media, you cannot control who sees your posts, but you can increase the chances of reaching the right people by this simple recipe:

  1. Find out who are the important people in your field
  2. Follow them on Twitter and LinkedIn
  3. Tag them to posts of both platforms.

The last point doesn’t always yield results, but I’ve also had some good experiences by including the Twitter handle of a person I know is working on the topic I’m writing about. Remember, you are not spamming but asking for their opinion. That is perfectly fine.

7. Track and optimize

This is perhaps the most important thing. Just like in all digital marketing, you need to work on your profile and social media activity constantly to get results. The competition is quite high, but in the academia, not many are fluent with social media marketing. So, as long as you put in some effort, you should get results relatively easier than in the commercial world! (Although, truth be told, you are competing with commercial content as well.)

How to measure social media impact?

  • choose metrics
  • set goals
  • track & optimize

For example, you could have reads/downloads as the main KPI. Then, you could have the goal of increasing that metric +30% in the next six months. Then, you would track the results and act accordingly. The good thing about numbers and small successes is that you become addicted. Well, this is mostly a good thing because in the end you also want to get some research done! But as you see that your posts get some coverage, it encourages to carry on. And gradually you are able to uplift your social media impact.

A research group could do this as a whole by giving somebody the task to summarize social media reach of individuals + the group as a whole. It would be fairly easy to incentivize good performance, and encourage knowledge sharing on what works. By sharing best practices, the whole group could benefit. Besides disseminating your research, social media activity can increase your citations, as well as improve chances for receiving funding (as you can show “real impact” through numbers).

The tool recommended by Luis is called Altmetric which is specifically tailored for research analytics. I haven’t used it before, but will give it a go.

Conclusion

The common theme is sharing your knowledge. In addition to just posting, you can also ask and answer questions on social media sites (e.g., on ResearchGate) and practitioner forums (e.g., Quora). I was able to beat my nemesis Mr. Valtteri Kaartemo in our Great Dissertation Downloads Competition by being active on Quora for a few weeks. Answering Quora questions and including a link in the signature got my dissertation over 1,000 downloads quickly, and since some question remain relevant over time, it still helps. But this is not only about competitions and your own “brand” but about using your knowledge to help others. Think of yourself as an asset – the society has invested tremendous amounts of time, effort and money into your education, and you owe it to the society to pay some of it back. One way to do that is sharing your knowledge on social media.

I still remember one professor saying a few years ago she doesn’t put her presentations on Slideshare because “somebody might steal the ideas”. But as far as I’m concerned, a much bigger problem is that nobody cares about her ideas. We live in a world where researchers compete against all sources of information – and we must adapt to this game. In my experience, the ratio of effort put in conducting research and communicating it is totally twisted, as most researchers lack the basic skills for social media marketing and hardly do any content marketing at all.

This is not only harmful for their careers, but also to various stakeholder groups that miss the important insights of their research. And I’m not only talking about popularization, but also other researchers increasingly rely on social media and search engines for finding relevant papers in their field. Producing high-quality content is not enough, but you also need to market your papers on social media. By doing so, you are making a service to the community.

Readings

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

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/

Defining SMQs: Strategic Marketing Questions

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.

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.

Customers as a source of information: 4 risks

Introduction

This post is based on Dr. Elina Jaakkola’s presentation “What is co-creation?” on 19th August, 2014. I will elaborate on some of the points she made in that presentation.

Customer research, a sub-form of market research, serves the purpose of acquiring customer insight. Often, when pursuing information from consumers
companies use surveys. Surveys, and usage of customers as a source of information, have some particular problems discussed in the following.

1. Hidden needs

Customers have latent or hidden needs that they do not express, perhaps due to social reasons (awkwardness) or due to the fact of them not knowing what is technically possible (unawareness). If one is not specifically asking about a need, it is easily left unmentioned, even if it has great importance for the customer. This problem is not easily solved, since even the company may not be aware of all the possibilities in the technological space. However, if the purpose is to learn about the customers, a capability of immersion and sympathy is needed.

2. Reporting bias

What customers report they would do is not equivalent to their true behavior. They might say one thing, and do something entirely different. In research, this is commonly known as reporting bias. It is a major problem when carrying out surveys. The general solution is to ask about past, not future behavior, although even this approach is subject to recall bias.

3. Interpretation problem

Consumers answers to surveys can misinterpret the questions, and analysts can also misinterpret their answers. It is difficult to vividly present choices of hypothetical products and scenarios to consumers, and therefore the answers one receives may not be accurate. A general solution is to avoid ambiguity in the framing of questions, so that everything is commonly known and clear to both the respondent and the analyst (shared meanings).

4. Loud minority

This is a case where a minority, for being more vocal, creates a false impression of needs of the whole population. For example, in social media this effect may easily take place. A general rule of thumb is that only 1% of members of a community actively participates in a given discussion while other 99% merely observe. It is easy to see consumers who are the loudest get their opinions out, but this may not represent the needs of the silent majority. The solution would be stratification, where one distinguishes different groups from one another so as to form a more balanced view of the population. This works when there is an adequate participation among strata. Another alternatively would be actively seek out non-vocal customers.

Conclusion

Generally, the mentioned problems relate to stated preferences. When we are using customers as a source of information, all kinds of biases emerge. That is why behavioral data, not dependent on what customers say, is a more reliable source of information. Thankfully, in digital environments it is possible to obtain behavioral data with much more ease than in analogue environments. The problems of it emerge from representativeness and on the other hand fitting it to other forms of data so as to gain a more granular understanding of the customer base.

Online ads: Forget tech, invest in creativity

Technology is not a long-lasting competitive advantage in SEM or other digital marketing – creativity is.

This brief post is inspired by an article I read about different bid management platforms:

“We combine data science to SEM, so you can target based on device, hour of day and NASDAQ development.”

Yeah… but why would you do that? Spend your time thinking of creative concepts that generally work, not only when NASDAQ is down by 10%. Just because something is technically possible, doesn’t make it useful. Many technocratic and inexperienced marketing executives still get lured by the “silver bullet” effect of ad technology. Even when you consider outside events such as NASDAQ development or what not, newsjacking is a far superior marketing solution instead of automation.

Commoditization of ad technology

In the end, platforms give all contestants a level playing field. For example, the Google’s system considers CTR in determining cost and reach. Many advertisers obsess about their settings, bid and other technical parameters, and ignore the most important part: the message. Perhaps it is because the message is the hardest part: increasing or decreasing one’s bid is a simple decision given the data, but how to create a stellar creative? That is a more complex, yet more important, problem.

Seeing people as numbers, not as people

The root cause might be that the world view of some digital marketers is twisted. Consumers are seen as some kind of cattle — aggregate numbers that only need to be fed ad impressions, and positive results magically emerge. This world view is false. People are not stupid – they will not click whatever ads (or even look at them), especially in this day and age of ad clutter. The notion that you could be successful just by adopting a “bidding management platform” is foolish. Nowadays, every impressions that counts needs to be earned. And while a bid management platform may help you get a 1% boost to your ROI, focusing on the message is likely to bring a much higher increase. Because ad performance is about people, not about technology.

Conclusion

The more solid the industry becomes and the more basic technological know-how becomes mastered by advertisers, the less of a role technology plays. At that point of saturation, marketing technology investments begin to decline and companies shift back to basics: competing with creativity.

Controlling ad quality in programmatic buying

Highway to ad quality.

Ad quality is an issue in programmatic buying where ad exchange takes place via computer systems. In traditional ad exchange, there’s a human supervising the quality of advertising, but in a programmatic system it’s possible to receive spammy, illegal, or otherwise undesirable advertising without publishers (ad sellers) being aware of it. Likewise, the quality of performance such as clicks, likes or even impressions might be compromised by fraudulent bot behavior.

In the lack of humans, how to control for quality? Well, some ways include:

  • bot detection — this is what Google uses to filter invalid clicks likely caused by bots. It includes i.a. detecting anomalies in click behavior. Facebook, too, has mechanisms for detecting bots. How well these systems function should be from time to time audited by neutral 3rd parties due to the inherent problem of moral hazard by ad platforms.
  • performance-adjusted pricing and visibility — again, used by Google and Facebook in Quality Score and Relevance Score, respectively. What works cannot be wrong, essentially. The ads with the best response get the most views for the less money. However, this does not directly solve the problem of removing undesirable ads from the system.
  • reporting — again, both Facebook and Google enable reporting of ads by end users. This shows to advertisers as negative feedback – once negative feedback reaches a certain threshold, the ad stops showing. It is in a way crowdsourcing the quality control to the end users.
  • algorithmic analysis of ad content — for example, Facebook is able to detect nudity in the pictures and consequently disqualify them. This is among the best methods, albeit technically demanding, because machine can treat many millions of ad content units in batches. With constantly developing machine learning solutions the accuracy of automatic detection of undesirable content approaches human classifiers.
  • finally, we can have human fail-safe as a “plan B”. Again, both Facebook and Google use manual detection of click-fraud but also in treatment of advertisers’ complaints over refused ads. However, the solution is expensive and does not scale over millions of ad units, so it can be seen as a backup at best.

There – I believe these are the most common ways to control ad quality in modern programmatic advertising platforms. If you have anything
to add, please share it in the comments!

EDIT: Came across with another quality control mechanism: private exchanges. They effectively limit the number of participating advertisers making it manageable for a small number of humans to verify the ads. The whole point of the problem is that this works for a handful or so ads, but when there are millions of ad units, humans cannot be used as the primary solution.