One of the new frontiers for generating seamless in-store experiences for customers is the ability to identify an individual from the moment they walk through your door.
Knowing who a customer is in the physical world is the key to using data and artificial intelligence to personalize that person’s experience with your brand. Some banks in China have already implemented facial recognition technology to authenticate individuals at ATMs, and Bank of America is working with Samsung on a pilot program using facial biometrics.
Another application for facial recognition technology uses a touch screen that the customer interacts with when entering a bank branch. In its current iteration, this technology personalizes content using general demographic attributes, such as age or gender.
As AI technology evolves, companies will be able to use mobile apps, kiosks, and even voice analytics to identify specific customers. In the retail environment, a customer might, for example, interact with a digital assistant that can present personalized product options to the customer by leveraging the ability to instantly access data about that individual’s preferences and past purchases.
Burberry, the British fashion brand, is an example of a retailer that is already leveraging big data and AI to optimize customer experiences. Burberry’s program is using data that customers have agreed to share by participating in one of the company’s loyalty programs, according to a recent report by Forbes.
The ability to analyze and act on massive amounts of data in real time has the potential to help increase sales. For example, if one of the store associates at Burberry knows that a shopper recently bought a specific suit or coat, that associate can help guide the customer to purchase a matching accessory item. In this case, the customer benefits from a highly personalized shopping experience, and the retailer gets a new sale.
The key to leveraging data in a brick-and-mortar environment is ensuring that you are analyzing and capturing data via every connected device, from mobile apps and tablets to touch screens and IoT devices. All the data gleaned from online customer integrations can be used to improve customer analytics, and to help formulate and assess key performance indicators related to in-store experiences.
The most valuable data from an analytics perspective is information about actual interactions with your brand by existing customers. This can range from information about the customer’s journey through your entire ecosystem to data on actual purchases. In addition to facilitating targeted marketing, this information can help establish benchmarks for measuring interactions, such as how much research the average online user does before entering a store to make a physical purchase.
Understanding what your data means and how it can be used to drive sales requires the ability to analyze it within a defined context. Advanced analytics is particularly valuable because it can provide the proper context to help companies understand key business data. It’s important to be able to determine, for example, just how profitable your blockbuster sales are on Black Friday. Maybe total in-store sales were much higher, but overall profit margin was lower when the cost of goods sold was incorporated into the calculation. Your ability to analyze aggregated and individual data can inform the development of key performance indicators, and help to identify any adjustments that can be made to better meet predefined sales targets.
The ability to define segments that are based on sequences and microconversions can help you identify unique clusters of customers with higher or lower propensities to purchase. Moreover, when offline activity and loyalty attributes can be incorporated into the dataset, the value of segmentation is enhanced even more.
Companies need a reference point for defining in-store success. That means identifying which metrics are the most significant, and measurable, in a physical location.
The ability to personalize in-store customer experiences can be optimized by defining reference points related to both what data is collected and how that data is used. That requires measurable goals that can be used as benchmarks to determine which data is the most meaningful in terms of meeting a company’s predefined objectives.
For instance, are Burberry sales associates selling more accessories to customers who they know made a recent purchase compared to customers whom they have no historical data about?
While it can be challenging to personalize experiences in a physical setting, the ability to access and use data in real time is a powerful tool for shaping customer experiences.
In addition to internal customer information, your analytics also should include external, third-party data. Combining your own customer data with additional demographic or purchasing data about an individual can provide more actionable insights when it comes to both target marketing and personalizing that individual’s experience with your brand.
Integrating all your data ultimately will provide an analytics tool that can be used to understand a customer’s buying behavior and preferences. Moreover, different data sets can be used to better segment specific markets, and to help align both online and in-store experiences with whatever factors have the most influence on a customer’s decision to make a purchase.
“It’s important to optimize every dollar that goes into your organization,” Rishi Dave, the CMO of Dun & Bradstreet told CMO . “Data can help you maximize those dollars by informing better decision-making and better alignment across the business, ultimately leading to faster growth.”
Harnessing the power of analytics at scale requires a thoughtful plan of action that enables you to start small and then analyze, learn, and adjust as you grow. The process may be challenging at times, but the rewards will be worth the effort.
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