Key to Successful Deployment of Data Analytics in Retail
Today, on these pages, we are going to discuss an issue that, over the years, has come to play a leading role in the business of retail – data analytics.
The immediate reason for this post is a special report – The Fine Art of Analytics – released by Boston Retail Partners, that speaks of problems yet to be addressed by those retailers who have gone ahead and implemented analytics in their business.
First things first, though. The special report does reveal that at least 44% of those surveyed had agreed that improved analytics was their top priority.
But guess what? The survey found that the ability to leverage analytics to improve business performance lagged intent due to a lack of organizational alignment and inconsistent processes.
Those are the keywords to watch out for after your retail business has deployed analytics – alignment and process. In the previous post, we had talked of the three big mistakes to avoid in marketing analytics. Here, we will look at the slipups that many data-wielding retailers continue to make.
Data analytics for an e-commerce business, or any other form of retail activity, provides advanced visibility into sales performance – by channel, by product. All of which helps in near-accurate planning and allocation decisions.
So what are the three biggest assets that a retailer has today? Simple – the people who run it, for the people who want it, and data!
Data must be looked at as the core asset of a retail business. And data analytics aligns all three resources to give the desired output, provided if it’s done right.
Here’s what retailers can do, really do, with these three assets arranged in a straight line – they can better understand the nature of their business, predict what’s to come, and better serve their customers. (Note, we’ve used the word ‘predict’, an explanation’s coming down the line).
The key, as we said before, lies in the implementation. Simply introducing analytics in your retail process will not work. Your organization must be aligned with it. How often have we read – data in silos does not work. That’s so true. Even if data is stored separately, they need to “breathe together”. Everyone in your business – from management to the storekeeper must not only have access to the very latest data but also be in the loop where day-to-day business decisions (except the core ones like profit and loss), are concerned.
Thus, organization alignment is the key to the successful deployment of data analytics. Without organizational alignment, it’s difficult to maximize the benefits that big data can bring.
The actual process of data analytics, too, has to be well-thought-out before deployment. While many slipups can happen, we will detail one here, which is quite a common one. Many retailers, even today, have physical stores alongside their online ones. Both produce their own sets of data, like the popularity of some items, how fast they are being sold, etc. Yet, if both are kept in separate silos, it would do grave injustice to your omnichannel marketing efforts.
Data from the physical stores must be allowed to interact with the other set of data obtained from your online business to get the big picture, to derive meaningful insights. Yet, a majority of retailers continue with this mistake.
Predictive Analytics in Retail
Analyzing data in retail should no longer stop “at the moment” but be used to anticipate consumer needs in the future. That’s where predictive analytics steps in.
Once dismissed as some kind of crystal ball gazing, predictive analytics is the science of forecasting future trends based on the study of present-day and past data.
This form of analytics makes informed decisions on what your customers may want in the coming days.
To read up on a more detailed version of the benefits of this form of analytics, you may want to read this earlier post:
An Engine That Drives Customer Intelligence
Oyster is not just a customer data platform (CDP). It is the world’s first customer insights platform (CIP). Why? At its core is your customer. Oyster is a “data unifying software.”
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