Monday, April 27, 2020

Here's a Game about Building Your Martech Stack

TL;DR: you can play the game here.

I’ve recently been running workshops to help companies plan deployment of their Customer Data Platforms. Much of the discussion revolves around defining use cases and, in particular, deciding which to deliver first. This requires balancing the desire to include many data sources in the first release of the system against the desire to deliver value quickly. The challenge is to find an optimal deployment sequence that starts with the minimum number of sources needed for an important use case and then incrementally adds new sources that support new use cases. I’ve always found that an intriguing problem although I’ll admit few others have shared my fascination.

As coronavirus forces most marketers to work from home, I’ve also been pondering ways to deliver information that are more engaging than traditional Webinars and, ahem, blog posts. The explosion of interest in games in particular seems to offer an opportunity for creative solutions.

So it was fairly natural to conceive of a game that addresses the deployment sequence puzzle. The problem seems like a good candidate: governed by a few simple dynamics that become interestingly complex when they interact. The core dynamic is that one new data source may support new multiple use cases, while different combinations of sources support different use cases. This means you could calculate the impact of different sequences to compare their value.

Of course, some use cases are worth more than others and some sources cost more to integrate than others; you also have to consider the availability of the CDP itself, of central analytical and campaign systems, and of delivery system that can use the outputs. But for game purposes, you could simplify matters to assume that each system costs the same and each use case has the same value. This still leaves in place the core dynamic of balancing the cost of adding one system with the value of enabling multiple use cases with that system.

To make things even more interesting and realistic, you could add the fact that some use cases are possible with a few systems but become more valuable as new systems come online.  It might be that their data adds value – say, by making predictions more accurate – or because they enable delivery of messages in more channels.

In the end, then, you end up with a matrix that crosses a list of systems (data sources, CDPs, analytics, campaigns management, and delivery systems) against a list of use cases. Each cell in the matrix indicates whether a particular system is essential or optional for a particular use case. Value for any given period would include: the one-time cost of adding a new system; the recurring cost of operating active systems, and the value generated by each active use case.  That use case value would include a base value earned by running the use case plus incremental value from each optional system. Using red to indicate required systems and grey to indicate optional systems, the matrix looks like this:



The game play would then be to select system one at a time, calculate the value generated as the period revenue, and then repeat until you run out of systems to add.  Sometimes you’d select systems because they made a new use case possible, sometimes you’d select because they added optional value to already-active use cases, and sometimes you’d select a system to make possible more use cases in the future. Fun!

I then showed this to a professional game designer, whose response was “you may have found the least fun form factor imaginable: the giant data-filled spreadsheet. I'm kind of impressed.”

Ouch, but he had a point. I personally found the game to be playable using a computer to do the calculations but others also found it impenetrable. A version using physical playing cards was clearly impossible.

So, after much pondering, I came up with a vastly simplified version that collapsed the 19 systems in the original model into three categories, and only required each use case to have a specified number of systems in each category. I did keep the distinction between required and optional systems, since that has a major impact on the effectiveness of different solutions. I also simplified the value calculations by removing system cost, since that would be the same across all solutions so long as you add one system per period.


The result was a much simpler matrix, with just six columns (required and optional counts for each of the three system types) and the same number of rows per use case (22 in the example). I built this into a spreadsheet that does the scoring calculations and stores results for each period, so the only decision players need to make in any turn is which of the three system types to select. Even my game designer grudgingly allowed that it “made sense pretty quickly” and was “kinda fun”. That’s about all the enthusiasm I can hope for, I suspect.

I’ve put a working version of this in a Google spreadsheet that you can access here.

Go ahead and give it a play – it just takes a few minutes to complete once you grasp how it works (put a ‘1’ in the column for each period to select the class of system to add during that period). Most of the spreadsheet is write-protected but there’s a leaderboard if you can beat my high score of 1,655.

Needless to say, I’m interested in feedback. You can reach me through LinkedIn here.
Although this started as a CDP planning exercise, it’s really a martech stack building game, something I think we can all agree the world desperately needs right now. I also have worked out a physical card game version that would have a number of additional features to make games more interesting and last longer. Who wants to play?







Thursday, April 02, 2020

A Dozen Market Research Studies on COVID-19 Business Impact

This sums it up.  From Bank of America via Twitter but I can't find a link to the original.
As marketers finish their initial emergency adjustments to coronavirus lockdowns, they are starting to think about longer-term plans. While the shape of things to come is impossible to guess, reporting on industry changes has become a marketing trend of its own. Here are a dozen-plus studies I’ve seen in the past week, most of which are on-going.

Retail Behavior Data

Adobe this week launched their Digital Economy Index, a long-term project that gained unexpected immediate relevance. The index draws on trillions of Web visits tracked by Adobe systems to construct a digital consumer shopping basket tracking a mix of products including apparel, electronics, home and garden, computers, groceries, and more. The headline finding of the initial report would have been a continuing drop in prices driven by electronics, but this was overshadowed by short-term changes including a 225% increase in ecommerce from March 1-11 to March 13-15. Online groceries, cold medications, fitness equipment and computers surged, as did preordering for in-store pickup. Extreme growth was concentrated in hard-hit areas including California, New Hampshire and Oregon.

Customer Data Platform vendor Amperity reported a less rosy result in its COVID-19 Retail Monitor, which draws data from Amperity’s retail clients. They report that total retail demand fell by 86% by the end of March and even online revenue is down 73%.  Food and health products fell after an initial stock-up surge in mid-March.

Retail foot traffic tracker Placer.ai has packaged its in-store data in a COVID-19 Retail Impact tracker, which not surprisingly shows an end to traffic at shuttered entertainment and clothing outlets, near-total drop at restaurants, and mixed results for grocery stores and pharmacies. Results are reported by day by brand, if you really want to wallow in the gruesome details.

Grocery merchandising experts Symphony RetailAI have also launched a COVID-19 Insights Hub, which reports snippets of information with explanations. These range from obvious (consumers are accepting more product substitutions in the face of stock-outs) to intriguing (canned goods sales rose twice as much in the U.S. than in Europe because of smaller families and less storage space).

Retail Behavior Surveys

Showing just how quickly the world changed, retail consumer research platform First Insight found that the impact of coronavirus on U.S. shopping behavior doubled between surveys on February 28 survey and March 17. In the later survey, 49% of consumers said they were buying less in-store and 34% were shopping more online. Women and baby boomers went from changing their behavior slightly less than average in the first survey to changing slightly more than average in the second.

Ecommerce platform Yotpo ran its own survey on March 17, reaching 2,000 consumers across the U.S., Canada, and United Kingdom. They found consumers evenly split between expecting to spend more or less over-all, with a just 32% expecting to shift purchases online. Food, healthcare, and, yes, toilet paper were high on their shopping lists.

The situation was clearer by the end of March, when Retail Systems Research surveyed 1,200 American consumers for Yottaa. By this time, 90% were hesitant to shop in-store, 94% expected online shopping will be important during the crisis, and their top concerns were unavailable inventory, no free shipping, and slow websites. (Really, no free shipping?) More surprising but prescient, given Amazon's labor troubles: just 42% felt confident that Amazon could get their online orders delivered on time.

Media Consumption

Nobody wins any prizes for figuring out that Web traffic went up when people were locked down. But digital analytics vendor Contentsquare did provide a detailed analysis of which kinds of Web sites attracted more traffic (supermarkets, media, telecom, and tech retail) and which went down the most (luxury goods, tourism, and live entertainment) in the U.S., UK, and France. Week-by-week data since January shows a sharp rise starting March 16. Less easily predictable: supermarket and media conversion rates went down as consumers spent more time searching for something they wanted.

Media tracking company Comscore has also weighed in with an ongoing series of coronavirus analyses. Again, no surprises: streaming video, data, newscasts, and daytime TV viewing are all up. Same for Canada and India, incidentally.


You also won’t be shocked to learn that Upfluence found a 24% viewing increase in the live-streaming game platform Twitch in Europe. Consumption growth tracked national lockdowns, jumping in Italy during the week of March 8-14 and in France and Spain the week after.

Consumer review collector PowerReviews has its own data, based on 1.5 million product pages across 1,200 Web sites. Unlike Contentsquare, they found traffic was fairly flat but conversion rates jumped on March 15 and doubled by March 20. Their explanation is people were buying basic products that took less consideration.  People read many more reviews but submission levels and sentiment were stable. Reviews were shorter as consumers likely had other things on their minds.


Influencer marketing agency Izea got ahead of the game with a March 12 survey, asking social media consumers how they thought they’d behave during a lockdown. More social media consumption was one answer, with Facebook and Youtube heading the list. Izea also predicted that influencer advertising prices would fall as more influencers post more content.


Consumer Attitudes


Researching broader consumer attitudes, ITWP companies Toluna, Harris Interaction and KurunData launched a Global Barometer: Consumer Reactions to COVID-19 series covering the U.S., UK, Australia, India, and Singapore. The first wave of data was collected March 25-27.  People in the U.S. and India were generally more satisfied with how businesses had behaved and more optimistic about how quickly things would return to normal. But U.S. respondents ranked support from the national government considerable lower than anyone else.


Edelman Trust Barometer issued a ten market Special Report on COVID-19, although the data was gathered during the good old days of March 6-10. Even then, most people were following the news closely and 74% worldwide felt there was a lot of false information being spread. Major news outlets were the primary information source everywhere (64%) but the U.S. government was by far less relied upon (25%) than anywhere else (31% to 63%). Interesting, people put more faith in their employers than anyone except health authorities. They also expected business to protect their workers and local communities.


Kantar Media has yet another COVID-19 Barometer, although they reserve nearly all results for paying clients. The findings they did publish echo the others: more online media consumption, low trust in government, and expectation that employers will look after their employees. Kantar says that just 8% of consumers expect brands to stop advertising but 77% want advertising to show how brands are being helpful, 75% think brands should avoid exploiting coronavirus and (only?) 40% feel brands should avoid “humorous tones”.

Survey company YouGov publishes a continuously-updated International COVID-19 Tracker with timelines on changing opinions in 26 countries.  Behaviors including avoiding public places and not going to work change quickly; others such as fear of catching coronavirus and wearing masks move more slowly.  Other attitudes have barely shifted, including avoiding raw meat and improving personal hygiene.  The timing of changes correlates with the situation in each country.

Job Listings


There’s also an intriguing little niche of companies offering job information. PR agency Global Results Communications just launched a COVID-19 Job Board to help people find work.  So far, it's not very impressive: as of April 1 it had under 100 random listings from Walmart to Metrolina Greenhouses to the South Carolina National Guard.


Tech salary negotiators Candor (did you know that was a business?) has a vastly more useful site, listing 2,500+ companies that are reported to be hiring, freezing hiring, rescinding offers, or laying people off. At the moment, half the companies on the list are hiring. The site offers a very interesting break-down by industry: transportation, retail, consulting, energy, and automotive are in the worst shape. Defense, productivity and education software, and communications are doing the best.