I'd Love To Use AI, But We Don't Have Enough Data

I'd Love To Use AI, But We Don't Have Enough Data

Leveraging an artificial intelligence (AI) software for analysis is on its way to becoming a standard business practice today. Companies are investing substantial resources into getting successful AI programs off the ground. Moreover, 91 percent of executives believe that AI will help them outpace the competition.

Yet, as companies assess potential AI strategies, they often run into one of two problems: They either don't have C-suite buy-in or they have too little data. But here's something for you to consider: Organizations often assume they need vast quantities of data to feed into AI, which is a myth.

In fact, when it comes to AI programs, small data sets can be quite useful. Those small amounts of data can enable your organization to uncover actionable insights about your customers, refine your target marketing strategies, and improve your business operations — all at a manageable cost through the use of automated analytics tools. The first step is to simply get started with data you have.

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Simple Data, Powerful Insights

The truth is that most companies already have the data they need. Generally speaking, you're ready for AI if you have data that meets four criteria: The data is labeled, accessible, easy to access, and describes an area of the business that the company hopes to improve — most often this is a company's customers and their engagement with them.

You can typically get such data from three commonly available business sources:

1. A simple list of customers - This list helps AI programs, at a basic level, to understand who your customers are — both current and potential.

2. A customer relationship management system (CRM) - By tracking simple characteristics of customers, like geographic location or purchase frequency, you give your AI analytics platform a holistic view of your audience.

3. A way to track orders - This helps an AI program understand how customers engage with your company — purchasing which products, at which times, at what price. An order tracker can be as simple as a cash register or a point-of-sale (POS) system, or as sophisticated as cutting-edge order management software.

From these simple data sets — which nearly every company has on hand — you can leverage AI analytics software to generate powerful insights. Such programs, according to TechRepublic, offer a range of ways to help businesses grow. For instance, AI software can help you understand customer behaviors, influence Net Promoter Score (NPS), or maximize promotions by promoting the right products to the right customers, at the right price.

For example, check out online vintage clothing company ThreadUp as they use AI to track customers' purchase preferences, enabling the brand to bundle products together that fit a customer's sense of style. Similarly, Stitch Fix, another online retailer, uses AI to analyze customer purchasing habits to provide personalized style recommendations to those customers. While these solutions may sound complicated, most companies, even with simple data, can incorporate sophisticated AI-driven solutions into their everyday operations.

Data in Action

To better understand how you can use simple business data for AI analysis, consider point of sale data. Using a small amount of this data — think hundreds of customers, compared to the millions often required for AI analysis — retailers can generate numerous pieces of actionable information about their customers. For instance, this could include the purchasing habits of customers, the churn rate of specific customer segments, or even predictions for ideal future product recommendations for frequent customers.

Imagine a pizza shop using such data, as an example. Using only customers' phone numbers and orders — a pizza and side dish, for example — AI software could find that when a customer orders wings, they tend to upsize their pizza order. The pizza shop could then leverage this insight to start a coupon strategy, providing customers with coupons for side dishes and wings to incentivize them to upsize their pizza orders.

Once companies have insights like these, in other words, they can double down on them — emerging with specific, actionable steps to improve their customer relationships and increase conversions. A company might, for example, analyze the same POS data and discover, using AI and business intelligence software, that if it increases its coupon use for a specific segment of customers during November and December, it can boost its repeat customer by 8 percent during the holidays.

The applicability of AI-led analysis, even using small or simple data sets, is practically limitless. A company using that same POS data could, for instance, also segment its customers into purchasing categories for targeted marketing campaigns. For example, analyzing its POS data, a business might find that different households tend to purchase different items. A new family, for example, purchases children's clothing and toys, while an outdoor enthusiasts favors athletic apparel and equipment. Better understanding customers enables company's to launch targeted campaigns to those customers — enticing them to purchase with customized product bundles and select coupon offers.

The upshot is that, while AI-led analytics often seems out of reach, virtually any company, with even a basic level of data, can develop an AI strategy that yields valuable insights immediately. You really don't even need to assess whether you have enough data for an AI program. You just need to get started with the data you already have.

To learn more about how an automated analytics software, like Ki, can help your business and team right now, request a demo and see how your data can be turned into action, today.

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