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Goodbye Hunches, Hello Predictive Analytics

Goodbye Hunches, Hello Predictive Analytics
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Change starts with an acknowledgement that things aren’t working. And perhaps it’s because of the continued onslaught of bad news in retail this year, but the industry collectively seems to be getting more honest about its shortcomings.

Case in point? A few weeks ago when Urban Outfitter’s senior management led analysts through their Q2 results, they showed remarkable candor into where their problems lie. Trish Donnelly, Global CEO said, “It became very apparent that our product lead times were significantly inhibiting our ability to make changes as quickly as we wanted. Although URBN has never been known as a long lead-time business, customers want and expect even more newness.”

Donnelly’s admission echoes what other firms are finding as well: too often speed to market is a barrier to growth. By adopting a new model that includes predictive analytics and testing, brands can move faster and be much more sure-footed before going all in on new products.

Try before you buy

The notion that you can find out how well a product will do before you’ve fully committed to it might seem impossible in the face of today’s globally complex supply chains. Yet an all-or-nothing approach to inventory risk management is precisely the problem retailers need to address. So what are the tactics by which retailers can measure and predict product success before they go all in?

There’s nothing wrong with gut instinct, per se, but historically half of new product launches fail, so wholly relying upon a hunch when there is a world of data sources to draw upon is short-sighted and commercially reckless. So let’s say you want to test an idea; let’s map out two scenarios that are data-informed and process-driven. In both scenarios, brands will need to start with clear goals by which they can measure relative success or failure.

In scenario one, you have an internal benchmark from which you can reasonably draw parallels. Your testing and forecasting will include historic sales, which should be parsed out and analyzed by full-price versus discounted sales, geographies and customer demographics. And let’s not forget that your business doesn’t exist in a vacuum so you must take into account your competition’s category, product and pricing strategies.

Scenario two is a bit more challenging because in this case, you’re entering the unknown with little to no sales history or competitor data. But the good news is there’s probably more data for you to draw from than you think. This is where identifying trends is crucial. Trends may have the bad rap as the offspring of gut instinct, but in today’s brave new world, data married with trends is the next frontier of predictive analytics. The types of data that you can draw from to reinforce your predictions include: relevant socioeconomic shifts (how consumer needs and the world we live in are changing), customer search data (for what and where are people looking), and how customers are talking about things (what is the social media chatter). The unknown can become much more known when you arm yourself with the right data.

With your data in hand, it’s time to test, but what are your variables? Of course, there are the product features themselves, but you also have the levers of price, channel availability, target demographic, geographic location, and even product messaging in your testing toolkit. It’s also important to gauge from your feedback the relative importance of variables such as price and product features in the overall purchase consideration process. The more important that variable is to the consumer, the less margin for error you have in your execution.

The feedback loop

So you’ve put your great idea out there, and you want to get a quick read on how it’s performing. Gone are the days where getting performance data in months was acceptable. Today’s market requires instantaneous and actionable feedback. This is where the value of e-commerce comes in. Online sales allow you to measure customer feedback immediately.

What should you be measuring online? If you log onto brand sites like Gap, Kohl’s and Dillard’s, you will see a dialogue box offering up an opportunity to give instant feedback on their web, in-store and the product experiences. What they are tracking is their Net Promoter Score (NPS), and while deceptively simple, the NPS tells you how likely someone is to recommend your products. Your score is calculated by subtracting detractors (those who would not recommend) from those who are your promoters (your advocates). With one additional follow-up question of ‘why,’ you can actually map out the wins and losses and make the necessary adjustments to your product. This scoring should take place early and often, to keep pulse on changes in your score that could ultimately penalize your bottom line.

Another readily available source of intelligence should be your social media channels; in the same way in which the NPS tells you who is most and least enthusiastic, so are those who take to their Twitter, Instagram and Facebook accounts to vocalize disappointment or satisfaction with their experience. These individuals are your ideal focus group and those with whom you should be engaging into deeper dives focusing on their underlying needs and expectations.

Armed with data as you’re developing your product and consumer feedback when you soft launch it, you get to sample a larger opportunity and perfect that recipe before committing resources. In a challenging retail operating environment where the stakes are high and your competition in constant motion, a systematic approach to testing and gathering customer insights has never been more critical.

This post was originally published on Sourcing Journal on September 15.

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