by Michael Ross, Co-Founder and Chief Scientist at DynamicAction
The time for moving to data-driven retail decisions is here. Consumers have almost unlimited choice and information. They are using a range of devices and being bombarded by digital marketing. They are shopping in new and complex ways.
Behind the scenes, the digital world is powered by a breathtaking array of technologies, each with its own set of operating rules. Many also require completely new types of decisions—such as programmatic marketing, bids on Google/Facebook, personalised landing pages and website merchandising. All of this activity creates a vast digital exhaust—the big data trail of every decision, impression, click, search, view and transaction.
Any retailer, whatever its size or focus, has to work with this data in new and smarter ways. For data-led businesses, data science is simply how they have always run their businesses—it is part of their corporate DNA. The challenge now is for traditional retailers to embrace data science and the need to think differently.
Here’s where to start; what needs to change; and what success looks like.
Driving change in an organization is difficult. As we have seen from the ongoing struggles of traditional retailers versus Amazon, retailers must innovate or get left behind.
There are 5 principle actions that are core to becoming data-driven retailer. These directives must come from the top down.
1. Put data-driven retail on the CEO’s agenda
If data is not on the agenda, any data-led initiatives are highly likely to fail. Data is a source of innovation. The enterprise must see retail data as a catalyst for thinking differently. If you end up making the same decisions, on the same frequency, in the same silos, with the same data, with the same logic, by the same people with the same incentives…nothing will change.
2. Get more out of your existing retail data, even if it’s not perfect
Data is a critical corporate asset that needs to be discovered, mined, extracted and refined to turn into something useful, and then be joined across systems. In particular, data cannot reside in a system or organizational silo.
“Good enough” data is hugely valuable. Data does not need to be perfect or complete, but it does need to be insightful and studied to develop a clear path to action.
Make sense of the digital data tsunami. The plethora of digital data can be overwhelming, but it is critical to understand customer intent and exposure. Understanding what’s useful is not necessarily straightforward.
3. Make “good enough” decisions
The objective is not to achieve perfection, but should be “how do we make the best decision possible given the available data?”
Celebrate mistakes as an opportunity to learn. It is impossible to improve if the retailer conceals failure and hides waste. The key is to minimize the loss, learn quickly and avoid making the same mistakes again.
4. Actively manage the change to data-driven retail
Managers may not immediately encourage new approaches to data, as these new approaches can be unfamiliar or threatening.
Be clear on who the decision maker is. Everyone has an opinion about data. It is typical that any discussion around data ends up with 20+ people in the room with varying opinions, subtly conflicting objectives, and no obvious decision maker.
5. Instill a culture of analytical curiosity and constructive challenge
Many people think they are “good at data.” In practice, the best data scientists are the most humble. Data science is hard, messy, easy to get wrong and easy to misinterpret. Beware of certainty and defensiveness.
Averages are the enemy. They are often misleading and rarely representative. Outliers, deciles, dimensions and stratification are critical tools for unraveling averages. Whenever you are presented with an average/ratio/percentage, a good question to ask is “what’s the distribution”?
The devil is in the detail. Retail leadership teams often become detached from the detail. In the digital world, the aggregated or simple story will often muddle the real story.
It is easy for business managers to ask unanswerable questions, and easy for data scientists to do clever analysis that does not drive any decisions. When it comes to data-driven retail and real transformation, the key principle is to establish a clear direction and process for everyone in the retail organization.
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