Why retailers are increasing their investments in data infrastructure and advanced analytics


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As organizations continue their economic recovery efforts from the woes of the pandemic – and many look for new ways to gain competitive advantage – there is growing interest in advanced data infrastructure and analytics tools.

Most in demand are data tools that enhance predictive and behavioral analytics and enable real-time data analysis.

One industry that is investing heavily in data infrastructure and analytics is the retail sector, including the convenience store segment. If that sounds surprising, consider this: as the country moves toward phasing out fossil fuel vehicles, which will eliminate a significant portion of the industry’s revenue stream, a large percentage of convenience stores are selling fuel, and it’s usually the biggest money maker.

To better understand where retailers are investing, VentureBeat spoke with David Thompson, Founder and CEO of 3 Leaps LLC, a company that helps businesses accelerate and scale automation using a data-driven approach.

Doesn’t everyone have their own data scientist?

While it’s hard to generalize, Thompson said the key drivers of data infrastructure investments have been to increase retail channel performance through higher move frequency and higher traffic rates. higher basket. As the name suggests, the term “cart rates” refers to the number of items a customer places in their cart, whether it is an actual cart or a digital cart.

“In some subsectors, there has also been significant investment in live chat or other customer engagement tools to increase responsiveness and reduce cost of attendance, Thompson said.

The first question, Thompson said, his organization is typically asked by potential customers is, “How can these technologies help us better understand our customer base?” Or questions about how technology is “driving investment in customer segmentation, promotional planning, and pricing.”

“Most of the retailers we work with seek a degree of ‘measured automation’, where routine decisions can be made by a system and outliers can be brought to the attention of an expert for personal review” , did he declare. “Today we see retailers in many industries hiring their own data scientists, implementing initiatives either on their own or to extend third-party solutions. The challenges of “rules-only” static forecasting models have become painfully clear with the supply chain disruptions caused by the pandemic.

He added that the company is now “…seeing more investment in what we call ‘classification’ and ‘interpretation’ technologies, where we use NLP. [natural language processing] and advanced media recognition in support of live chat and transcript “sentiment analysis” to expand and improve our reach to customers.

Using data infrastructure enhancements at every supply chain disruption

The greatest impact of strengthening data infrastructure for many retail sectors has been seen in supply chain optimization. This can cover anything from restocking to assortment planning, depending on the retail sector.

For retailers with a multi-channel strategy, the priority may be to help the retailer better understand the benefits and costs of complex fulfillment options such as “order online, collect in store” or to consider multiple fulfillment strategies. delivery.

“Finally, we’re seeing that e-channel retailers in particular have invested in tools to automate very fast competitive responses — what we sometimes call ‘dynamic pricing,'” Thompson said.

Although the foundations of such competitive indexing are rule-based, the approach often requires weights or strategy inputs built from various artificial intelligence (AI) or machine learning (ML) processes to finalize the answers.

The best programs, in Thompson’s experience, focus on measurable success criteria that include specific measures of error as well as procedures for dealing with the “unknown” cases that inevitably arise.

“Conversely, a lack of attention to these areas will almost certainly lead to implementation failure,” Thompson said. “User trust, once lost, is incredibly difficult to regain. Starting with a subset of the business and spending more time measuring results will help build confidence that the benefits will scale with the program. »

Enjoy the benefits of advanced analysis tools

According to Thompson, retailers hope to benefit from investments in data infrastructure and advanced analytics tools in two main areas: supporting growth and increasing productivity.

“First and foremost, AI/ML tools and applications can help us understand our environment and customer base faster and more thoroughly,” Thompson said. “This knowledge can then be used to more effectively assess potential strategies. With the economies of computing these days, we can also consider a wider range of possible strategies than in the past, with much less manual work.

“Second, we can reduce costs and improve retention through better quality of service. Eliminating unnecessary paper handling makes people happier,” Thompson said. “Being able to assess every interaction helps us improve our training and responsiveness. Knowing more about what a particular day is will help us better position the workforce we bring to a particular situation.

3 Leaps LLC consultants focus heavily on predictive analytics when discussing advanced technologies in retail, and for good reason.

“Digital Workflows and RPA [robotic process automation] can provide huge benefits in terms of accuracy, data security and reduced overhead. These solutions typically rely on AI/ML solutions for image, text and even speech recognition,” he said.

Going paperless has become a cliché, but Thompson stressed that it really should be every organization’s goal. “Smart forms”, digital ID methods and other tools can allow employees to perform complex workflows containing sensitive information from almost anywhere, saving money and increase productivity.

“Multi-format chat and NLP tools have come a long way in recent years. Properly deployed, these technologies can help both customers and employees in directed research [such as] ‘Where can I find…?’, ‘How can I…?’ and training,” Thompson said.

New applications are also emerging for employee training and coaching, whether through similar transcript analysis or through simulated live interactions.

“Expect this field to grow significantly over the next few years in industries such as ours with high education requirements and a need for regulatory or statutory compliance verification,” Thompson said.

Increase “comfort” with state-of-the-art technological tools

Thompson’s organization sees the use cases growing as more companies become comfortable with an increased role for classification and prediction technologies.

“What we would like to emphasize is the importance of having strong processes in place for data validation and testing,” Thompson said. “Think of the concrete examples we have seen stemming from the pandemic. Forecasting models have broken – badly, in some cases – due to a drastic change in buyer demand, a break in the supply chain, or both. Successful use of technologies requires periodic review and specific checkpoints built throughout the processes to let go [or at least warn users] when the data deviate too much from the expected norms.

Just as organizations have “A/B” testing to gauge the impact of price or assortment changes, they also need “A/B” testing for model quality, Thompson believes.

“We recommend that you ask your design teams, partners or suppliers to deliver and use [regularly] such a harness. By running known historical data against the current system and a planned upgrade, we see the actual differences in output that come from the changes,” says Thompson. “With such techniques, we build confidence both in the quality of our outputs and in the procedures for handling unknown or unexpected results. Unstable models will be quickly rejected by our professional users for a good reason – it is not useful to be right once in a while and to be wrong most of the time.

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