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How To Use Data for a 3D View of Your Customers

Strategy   |   Anne-Queline Keller   |   Apr 16, 2020

Retailers by their very nature are data driven, but being data driven is not enough to win in today’s hyper competitive market. Retailers must move beyond traditional data analysis to uncover the hidden insights that create competitive edge.

Taking a novel approach to big data analysis allows retailers to build dynamic shopper profiles that can be used to differentiate the brand and its services.

To uncover how retailers can best leverage customer data to get a true three-dimensional view of their shoppers, Alteryx’s senior manager, solution marketing, Anne-Queline Keller, sat down for an exclusive Q&A on the topic.

Q: What are some of the biggest challenges retailers face today as they look to become data-driven organizations?

Retailers and e-commerce sites have been creating volumes of data for many years, and they understand that there is money hiding in that data. Today’s retailers are already savvy about how they are using their data, whether that be for assortment or evaluating new locations for store placement or expanding their brand. But as the same retailers and e-commerce sellers look to mature their approach to compete and keep up with the fast-paced changes in the industry, they need to move beyond the current use of analytics and data and look to gain efficiencies by collapsing functions. They need to ask, “Can I use the ‘not so obvious’ insights in order to create a competitive edge in the market?”

What are the “not so obvious” insights that can be gained about their market and their customers to challenge the status quo? Beyond looking at the data they’re gathering and creating through multi-channel selling, these channels are creating a web of information. How can you build a narrative around online and offline data points?

When you look at all that information, there are depths of details and other attributes that need to be thought of as a point within a web and you need to create a three-dimensional view of it. The data you are capturing is good, but it’s not good enough, so the challenge is how do you put this data into a three-dimensional context such as time, location, and sentiment. You must bring in additional external sources to derive business insights and see the “not so obvious.”

As retailers and e-commerce sellers compete in this complex, time-driven market — where days have gone to seconds, where spending hours at the mall has turned into placing a quick order on Amazon — they need to shift their thinking. The premise of time has accelerated for everyone, which means everything must be collapsed, including reaction time, functions, conversion times, etc.

Q: How can retailers best combine data from a variety of consumer touchpoints to build meaningful and actionable customer profiles?

In order to get a three-dimensional view of multiple sources of data, retailers need to use GIS data (physical data), sentiment data (non-physical data) and physiological data (behavioral data and logical data) and for e-commerce sellers I would also add time. This is the new trifecta of data. In order to do this kind of modern retail analytics to move the needle and impact your bottom line, you NEED to have an analytics platform. A spreadsheet is not able to perform this kind of analytics. But not all analytics platforms are equal. The reason retailers have not done this in the past is primarily due to employee skill sets.

Many analytics teams do not have master’s degrees in mathematics and don’t have data scientists. This is the single reason most often cited by retailers as their struggle to adopt a new approach. But with a self-service analytics solution, you don’t have to be a data scientist, a statistician, or a python coder. This is removing the fear and the roadblocks for retailers to pull this three-dimensional data. All you need is to answer a question, “What problem am I trying to solve?” And with assisted modeling for predictive analytics, the ease of use amplifies. You don’t even need to understand algorithms. Alteryx builds them for you based on the data you are importing into the platform, and in minutes you are empowered.

Q: Big data insights are the key to making informed decisions across the retail enterprise. But unfortunately, many organizations hide data from those that need it most. How can savvy retailers unlock the power of insights for all?

How do you share this data and insights with other branches or departments? Users are looking to find ways to get out of the mundane reporting to providing high value business support. Once again finding the right analytic platform is key. Finding a platform that allows analysts to automate and post on-demand reports to users enables these same analysts to focus on high value, strategic business support. In addition, we spoke about time. Time has become the biggest factor between a retailer and their customer. Bringing real-time information to a local branch or store managers, can be critical to customer acquisition. The same is true for e-commerce. Real-time analysis of your e-commerce store can make all the difference to convert someone who is just browsing to a buyer.

Q: How has the ability to accurately predict customer demand forever changed supply chain operations?

Demand forecasting is still very traditional. Most retailers are using sales history to anticipate future sales forecasts, but very few are actually using modern predictive capabilities. Anticipation results in a 50/50 chance, which is pretty much a GOOD educated guess. What you really want to do is become more accurate with your forecast by augmenting your methodology with predictive modeling. This increases accuracy upwards of 75%+ that you will be closer to your actual number. This requires an analytics platform that is capable of handling a variety of predictive models that best suit your business. For example, you may need to consider seasonality within your models but on the other hand, seasonality needs to be normalized, and it’s critical to most retailers to get it right. In addition, you need a modern approach to predict demand, using a platform that helps you look at external factors that directly impact demand such as weather, location and demographics which are shaping modern forecasting. This ties back to the data trifecta of physical, non-physical and psychological data.

You must understand WHAT is driving the behavior so you can gain that three-dimensional view of your customer.

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