The Kepler platform was designed to address a vast array of Machine Learning (ML) use cases in retail, and its easy-to-use Automated Data Science Workflows ensure your enterprise can leverage the benefits of advanced ML within days, not weeks. Below, we outline how retailers can leverage Kepler’s capabilities to protect their bottom line and approach new challenges with the level of agility required in today’s market.
The seismic economic shift ignited by COVID-19 has brought particular challenges for retailers across the globe. From reduced hours and forced closures to major spikes in consumer demand for specific items, retailers have had to swiftly react to conditions that might change within minutes, all while demonstrating agility, openness, and care for their workforce and customers.
A new McKinsey report cites that, although many American consumers believe the economy will rebound in 2-3 months, 48% are completely cutting back on spending, while 59% are being careful about their spending habits. And, crucially, consumers only expect to increase their spending on “must have” categories such as groceries, while demands for at-home entertainment and household supplies are also expected to mildly increase.
Regardless of the retail category your business is in, chances are, these turbulent times have you focused on tactics and strategies that protect your bottom line, while ensuring your sales remain steady, and your employees remain safe and healthy.
There are a host of Machine Learning use cases for retail that can be leveraged to help you protect your business, your people, and your customer base. From nailing your product recommendations and reaching new audiences for your ecommerce sites, to optimizing inventory at your brick-and-mortar locations, we’ve rounded up a handful of the top ways ML, and Kepler specifically, can address today’s most pertinent challenges. And did we mention that Kepler requires zero machine learning experience to use? That means you can onboard the platform and start seeing results from these use cases and more within days, not weeks. For each use case, we called on one of our Data Scientists – Rodrigo Araujo – to describe how Kepler’s Automated Data Science Workflow solves each use case with ease and accuracy.
A recent report by eMarketer proves that retailers will have to continue to react swiftly and strategically to serve entirely new buyer groups via ecommerce platforms. Their survey of American consumers revealed that 86% of individuals aged 60 and older will avoid shopping centres during, and well beyond, the COVID-19 pandemic. This permanent shift in consumer behaviour requires a major realignment of targeting tactics to ensure new buyer groups are adequately served with relevant, personalized recommendations throughout their online buying journeys.
Furthermore, while brick-and-mortar stores are confronting a significant hit in an already shaky landscape, the uptick in online shopping presents a hopeful opportunity to capture new audiences. A Gartner report illustrates that luxury retailers that typically relied on department stores to drive sales now plan to recalibrate their strategies by focusing primarily on direct-to-consumer purchasing channels. The same report noted that 57% of fashion retailers expect conditions to worsen in 2020. And yet, Gen-Z, a digital-first consumer group, represents 46% of the population interested in purchasing premium goods. Taking this into consideration, it’s clear that the current climate presents a major opportunity for premium brands taking a hit from forced store closures to connect with younger consumers through relevant targeting.
For this specific marketing use case around customer targeting, Kepler’s clustering workflow can accurately and efficiently target ideal customers. Clustering is a classic unsupervised ML technique that’s arguably one of the most important. It has been leveraged for quite some time by data scientists, and, although there hasn’t been many recent major advancements in this area of ML, it’s an extremely effective approach for customer segmentation.
In customer segmentation activities, your main goal is to connect specific tactics to specific groups. With clustering, you can bundle data of similar “groups” and identify segments of the population based on their specific characteristics – such as age, location, previous purchases, spending frequencies, etc. Kepler’s Automated Data Science Workflow empowers you to make highly specific buyer groups at scale with complex data sets, delivering actionable insights so you can target them with the right products at the right time.
Retailers who haven’t been forced to shut their doors permanently are dealing with other challenges, including dramatic surges for certain types of products and significant dips in others. Nielsen has created a useful framework for this phenomenon, preparing retailers to brace for specific “stages” of buying behavior, including “pantry preparation” wherein stockpiling essential items like canned goods and toiletries accelerates rapidly, and “restricted living,” wherein consumers are limited to very specific parameters to purchase essential items. See the chart below.
Within these scenarios, retailers need to think strategically about managing their inventories, and be ready to react quickly, as news cycles and government restrictions invariably thwart consumers between one buying threshold to the next.
Most inventory systems rely on tabular data to track stocking needs. Kepler’s Automated Data Science Workflow – Times Series Forecasting – was created so any type of user, including those with no Machine Learning experience, can perform complex tasks with these types of data. Time Series Forecasting is all about predicting data that changes over time. With accurate inventory forecasting, you can protect your bottom line from sunk costs due to waste waste, or missed revenue from unfilled shelves.
With Kepler, anyone using the Times Series Forecasting workflow has the advantage of trying multiple different ML methods to get the best result. That means, if your inventory prediction accuracy is low, you can leverage a host of different algorithms to improve your predictions. Within Kepler, the best model is automatically chosen for you with the optimized parameters by leveraging both traditional ML and advanced Deep Learning (DL) models. Finding the right models to use to solve this type of ML problem is rather complex and time consuming, but with Kepler, you could perform it within hours, depending on the size of your data. With Kepler, retailers can glean insights to optimize inventory with greater proactivity and accuracy.
