Imagine you are the CEO of a retailer. The economy is roaring, people are starting to shop more at your higher end shops, increasing margin. Quarter over quarter top line revenue growth is coming in, albeit at a slow, measured growth rate. You are even getting some level of margin growth by implementing some measures to remove excess cost out of the business. Things are going great!
Then, one year, everything changes. A new competitor emerges. Similar brand strength, similar cost structure, even similar locations. However, they seem to get twice as much revenue per square foot, based on some new traffic pattern analysis that they are doing. And they seem to have very savvy staff who are empowered by their systems to recommend products for customers that they seem to actually want to buy. This, when combined with the investment they have made in omni-channel customer experience, is allowing this new upstart to drastically cut into your marketshare.
You are paralyzed by fear. You’ve read a few articles about big data, but given you are a brick-and-mortar retailer focusing on the high end, you did not think this was as much of a priority. You’ve been focused on cost control and squeezing an extra 10 basis points of margin out of your existing model, and your competitor has leapfrogged you by creating an entirely new model that increases margin 2%. In retail. Where such a margin increase can mean profits rise by 40%.
You used to be aware of analytics. Now you need to lead the charge to be analytics led, before your competitor ends up beating you so badly that you become an acquisition target – with your declining brand and your real estate becoming the only assets that remain. You are faced with a new imperative – how do I drive this organization to become analytics led, and in a way where I can “re-leapfrog” this new upstart competitor?
Becoming an analytics led company – a company that drives strategic advantage through analytics – is a journey that requires rethinking of how your entire business operates. It requires agility to be able to change tactics and strategies in response to data coming from how your customers interact with your products, retail locations, and your brand in general. You will not get there overnight. There are, however, traits that such companies share – traits that you can use as a marketer to know whether you are at least on the way to becoming such a company.
Contextual Intellectual Capital is Valued
For purposes of this discussion, contextual intellectual capital is the sum of learning from analytics that has taken place and exists in the minds of people actively involved in shaping the business. For example, a tuned collaborative filtering model that forms the basis of a recommendation engine, paired with data scientists who know how to evolve the model, could be considered such capital. It is contextual, because it’s value is derived from properties unique to the company – the brand, the people, the culture, the customer base. Even if you “copied the code” to a different company, it’s value would deteriorate, because it is optimized for that particular brand.
Analytics led companies have a great deal of contextual intellectual capital. It is the models, the learning, and the people who know how to leverage and improve the model. It is the ability to create new models in response to changing business conditions – that build on what is learned from prior models whose value is derived not just from the math, but the context they come from.
Driven by Data, But Intuition Still Matters
One of the more surprising things you find in analytics led companies is that, while they are naturally driven by data, as you would expect them to be, they do not completely discount intuition. Intuition is a unique capability humans have for processing lots of complex information from diverse sources in parallel. This ability is something that humans excel at much more than computers do.
What does this mean? A human will have the context to intuitively know, based on data on whether a model has worked (or not) how a model may need to be tweaked. Intuition will give us a safe harbor to know when results from a model that is new should be called into question. For example, if a new model for managing up-sell recommendations is having great results well beyond what it should, intuition will tell us to look at the data closer so we can know whether some one time black swan style event is influencing the recommendation.
For example, during a cold snap, more people may buy face-masks that cover your entire face while they are buying new winter coats – but that does not imply that such face-masks should always be up-sold, as this condition only exists perhaps when the temperature goes below 10 degrees Celsius. Models running 100% unmanaged, if they are not including weather in the analysis, would not pick up this detail, where a model combined with intuition is much more powerful.
Optimized for Learning
Analytics led companies usually think quite a bit differently about what the source is for sustainable value. At first glance, a competitor may think it is the presence of a killer model that tells them what customers want to buy with incredible accuracy. However, as valuable as that is, in a competitive market, competitors will quickly figure out ways to reverse engineer and replicate the model. It is not a static model that really provides the value in an analytics led company; it is the organizational capability to learn from and quickly adjust the model to changing conditions.
Imagine you are a retailer who has been selling products in primarily western economies for the last 10 years. As you move into emerging markets, how does the system that evaluates product mix change? Organizations that do not have agility to update and evolve models risk using inappropriate models for new conditions and situations. Companies that are optimized for learning can quickly adjust to new realities and change models to meet new business conditions.
Powered By Science
Is there any type of science that isn’t driven by data? Analytics led companies understand that data science is really *business science.* Such science has a process, and that process is the scientific method. You form hypotheses, you test them, and if they fail, you learn and move onto the next hypothesis. Data science as a term may be a fad, but the scientific method, and application thereof to business, is most certainly not.
Part of science, of course, is embracing failure of models, even if the ideas behind them come from a high ranking executive. Proving that models don’t work, in science, is just as important as proving that they do. In analytics led companies, while intuition matters (see above), when a hypothesis is falsified when under test, the outcome is accepted in favor of alternative hypotheses. The rank of the idea’s progenitor does not apply.
Practitioner Driven Tool Choice
In analytics led companies, the CIO does not buy analytics tools based on conversations that occur on a golf course. While tools are used, the data science team vets tool choice.
Thankfully, most data scientists tend to be very pragmatic about tool choice. The tools of choice these days tend towards free or open source when possible – things like Hadoop, R, Python, and related libraries. Paid, proprietary tools have their place in certain situations, but the defaults tend to be tools that lower the cost of experimentation, so that too much capital does not get spent in yet to be unproven ideas. Nobody wants to invest seven figures in tooling for a model that may not work – for at some point, too much investment in an unproven model will create pressure to “make it work,” even if it turns out to be wildly wrong.
Analytics Driven Strategy
The most important trait of analytics led companies – above all – is that there is confidence that science properly applied to business has potential to deliver breakthrough value. The executives have not only seen it in competitors or upstarts encroaching on their turf, but they are prepared to compete by executing an analytics strategy that plays to their own strengths.
Does this mean that brick-and-mortar retailers all replicate the strategy of Amazon? Of course not. It means a contextual strategy that plays to the retailer’s strength. If it is a retailer that has strong location coverage in certain kinds of communities, the analytics strategy will consider that. If it is a retailer that has a brand that appeals to a different kind of consumer, it will consider that as well. They deeply understand that context matters – and that the unique combination of analytics model, people, corporate culture, and brand for the basis of a successful analytics strategy.
Find out more about our Big Data Analytics practice.