Competition in the retail industry is fierce and fast moving. To grow sales and maintain your edge, you need to make sure you have the right products available, in the right channel, at the right time, for the right price. But finding answers to key retail merchandising and marketing questions isn’t always easy―firms need a solid retail merchandising strategy.
In my experience working with many leading retailers, I see them seeking to resolve the same recurring challenges: How do I determine what products or services shoppers want? How can I stay ahead of the curve and predict trends before they occur?
The good news is that shoppers generate data at astounding rates across the industry. Every time a customer browses your website, makes a call to your contact center, downloads an app, redeems an offer or orders a product, he or she leaves behind a trail of personal information. Properly harnessed, this data can reveal which channels customers prefer and which offers might generate the best response―information that is critical to sales and marketing success.
While retail merchandising is often viewed as an art, new technologies can now add science to those processes. By quickly identifying shifting trends, consumer sentiments and selling opportunities, retailers can make more accurate decisions that can help maximize marketing results and optimize return on inventory.
Improving retail merchandising decisions
Predictive analytics is increasingly taking the place of planning and forecasting guesswork, leading to tighter merchandising strategies and better financial performance. Among the predictive tools I’ve seen emerge in the past few years are data-backed product recommendations and decision engines, which are helping both online and traditional, brick-and-mortar retailers improve sales and optimize campaign results.
Retailers are also tapping new areas of intelligence by using an abundance of real-time, streaming data from multiple touchpoints to gain insights into a completely new area of the retail sphere: store layouts and shopper navigation. RFID tags on shopping carts (to track shopper movement inside stores), analysis of point-of-sale (POS) data and modeling techniques (such as market basket analysis) are helping merchandisers piece together the puzzle of how shoppers go about their shopping journeys—and why they buy what they do.
Embracing a new era in information science
Developing a core competency around advanced analytics is critical to retail success today, but it also provides an important framework for one of the next waves to hit retail merchandising―cognitive computing. Cognitive systems generate, not just answers to numerical problems, but reasoned arguments and recommendations about more complex and meaningful bodies of data. And they continually evolve based on new information, outcomes and actions.
Cognitive systems can analyze and identify patterns from structured information such as purchase history, as well as unstructured data such as call center feedback and social media. This provides you with more informed, actionable insights to localize a store’s assortments, promotions and layouts. Because these systems can engage in dialog with humans, they can understand customers based on past communication and behavior, and bring context- and evidence-based reasoning to interactions.
In my discussions with retail clients throughout the industry, I’m finding that many of them are keenly aware of the need to embrace cognitive computing to better engage with shoppers and differentiate their offerings. Recent research appears to support my findings. According to one study, 91 percent of retail leaders familiar with cognitive computing believe it will play a disruptive role in the industry, and 94 percent of those executives intend on investing in cognitive capabilities moving forward¹.
Charting a cognitive course
The path to effective cognitive computing requires a well-defined vision, aligned business objectives, and an integrated foundation of data, analytics and cloud technologies. Fortunately, your existing analytics systems and infrastructure can be augmented with cognitive technologies to make them smarter and more predictive. Here are a few points to consider when getting started.
Develop a cognitive strategy. Your specific goals must be established within the competitive context of your markets. Critical data sources must be identified, along with the services and processes that can fully benefit from cognitive technology. And experts must be available to train cognitive systems.
Set a secure, scalable and open technology foundation. To build cognition into the objects, products and systems that matter, your IT architecture must be open and stable. High-performance servers, efficient storage systems andhybrid cloud resources underpin this work, along with trusted security from the core to the edges of the network.
Evolve your infrastructure for smarter, faster analytics. Cognitive tools that learn and adapt make faster responses possible, but they require an IT infrastructure capable of managing this complexity and delivering insights at the speed of business. Faster infrastructure leads to quicker insights and the ability to make better decisions that can bolster your competitive position and enhance your bottom line.
Determine the business outcomes that matter most. Envision ways that cognitive computing could work with current merchandising, marketing and planning applications and their data sources to deliver actionable insights. Decide which stakeholders and decision-makers need to be involved and make sure to include them early in the discussions. Remember the core goal of your cognitive initiatives―uncovering the insight that translates to business value.
Retail merchandising is a natural fit for cognitive computing. Companies that recognize and adapt promptly to this reality will not only serve up better customer experiences, but can use this cognitive disruption to drive true brand differentiation and grow market share.
For more information on the advantages of cognitive technology in retail, check out this short video.
¹ Thinking Like a Customer: Your Cognitive Future in the Retail Industry, IBM Institute for Business Value study, 2016.
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