predictive artificial intelligence

How marketers are using predictive artificial intelligence in retail

In recent years predictive artificial intelligence has become a game-changer for financial services, healthcare, and other data-intensive industries. And while the retail industry has been slower to leverage predictive artificial intelligence, savvy marketers are catching on.

Brands are starting to understand how the use of predictive artificial intelligence can help them rake in greater revenues by transforming big data into actionable insights. Big data has made a huge impact on retail, as large volumes of data created by POS systems, in-store engagements, and mobile platforms continue to pour in. But finding patterns and insights within that data calls for some heavy-duty computing power—which is why companies are turning to artificial intelligence (AI).

Predictive Artificial Intelligence Helps Marketers Make Smarter Decisions

The retail industry creates a lot of data. But that data is useless when left unrefined. AI, combined with methods like statistical algorithms and deep learning, can predict future events with a level of detail and accuracy that goes beyond simple demand forecasting. Traditionally, predictive analytics uses automated tools created to only carry out specific tasks. They could only understand small amounts of data in predefined models unaffected by complicated variables.

Now, AI is more autonomous. It can produce decisions, run simulations that identify all future data points, and make sense of complicated variables. And combined with big data, artificial intelligence offers retailers more accurate predictions that form the basis of smarter decision making.

Predictive Artificial Intelligence Unlocks Data’s Full Potential

If you’ve heard the saying, “data is the new oil,” you may have wondered what it meant. Basically, data in 2019 is more valuable than it was back in 2009. This is because we’ve become much better at using data to make predictions. AI has made prediction-making cheaper, faster, and more accurate, meaning AI can be used as a new method of problem-solving.

Accurate predictions are the key to effective decision making. When it comes to marketing in retail, there are a lot of decisions to be made. One of the greatest benefits predictive AI offers the retail industry is the ability to learn from feedback, which enables AI to become better at creating intelligence. This is simply another way predictive AI creates value—it’s a tool that improves itself over time.

how brands are using predictive artificial intelligenceEvery decision that is enacted has an outcome. That outcome provides feedback about the initial prediction itself. For example, say AI predicted a pair of sunglasses would be a hot sales item in June, so a store made the decision to restock extra pairs. AI could be used to examine the data to learn how many pairs actually sold and confirm if the predictions were right or wrong. Knowing the accuracy of a prediction allows AI to make even more accurate predictions in the future. That ability makes feedback more valuable than historical data.

How Predictive Artificial Intelligence Can Solve Marketing Challenges

When marketers and retailers make the wrong decisions, it can lead to revenue loss. Over- and under-stock are classic examples, but there are so many other areas affected by poor prediction-based decisions; such as space plans, pricing, or knowing where to advertise.

Businesses should use predictive AI when trying to process complex, relational data. Predictive artificial intelligence can:

  • model merchandise planning scenarios; like what products to promote, how to set prices, or how much inventory to allot
  • simulate the outcomes of every possible decision that can be made about inventory forecasting, promotions, or pricing
  • plan advertising campaign details; such as what channels to advertise on, what products to feature in ads, or what demographics to target
  • understand the relationship between products and promotions, and their effect on sales
  • analyze all data and interrelationships between products
  • make predictions; such as predictions about customer buying behavior or the best time of day to restock shelves
  • profile user marketplace preferences and make helpful suggestions, such as holiday gift ideas

In summary, predictive AI can be used to discover a variety of connections in retail. From planning to analyzing customer feedback, predictive AI helps retailers better prepare for the future. And by making better decisions based on more accurate predictions, brands can delight customers with a more enjoyable shopping experience.

Shopkick Helps Brands Act on Intelligence and Helps Make AI Smarter

Technology has evolved beyond analytics and demand forecasting, and predictive AI is the future of business intelligence. But, once you have sophisticated predictions, how can you use them?

Brands can use predictive artificial intelligence to stay competitive through third-party partnerships.

Brands can use predictive artificial intelligence to stay competitive through third-party partnerships. Paired with predictive AI, a shopping app like Shopkick can be used to craft personalized messages and enhance the customer shopping experience. By using consumer data, the app can provide a personalized, virtual greeting to engage consumers at the start of their shopping trip, which keeps brands top of mind and increases sales potential. Shopkick helps brands get the most value out of predictive artificial intelligence while making those predictions even smarter.

Shopkick helps our partners reach consumers as they shop via our intuitive app with a highly active user base. To connect with our users and improve in-store sales, contact us.

Image courtesy of George Rudy



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Dima Volovik

EVP of Product and Engineering

Dima Volovik is the EVP of Product and Engineering at Trax Retail — Shopkick.

Dima Volovik is the accomplished product and engineering leader who led teams to deliver innovative and commercially successful e-commerce products, marketplaces, and enterprise solutions for Amazon, Comcast, Fandango, and Universal Music. Before joining Trax, Dima was the Director at Amazon, where he led product development and Engineering for Amazon Appstore and Amazon Prime Video, CTO at Fandango, and Paciolan, head of technology at Golf Channel/Golf Now, and Global VP of Direct to Consumer Technology at Universal Music Group. Dima’s expertise includes developing consumer products, marketplaces, and enterprise solutions.

Dima grew up in Baku, Azerbaijan, where he received his MS in Electrical Engineering from Azerbaijan Oil Academy, and he currently resides in Los Angeles, California, with his family.