As businesses continue to navigate how to reach customers in a post-COVID world, it can be difficult to gain back consumer confidence. Consumers are adjusting to new routines. Many have adjusted their shopping habits as a result of the coronavirus pandemic. More consumers are shopping online, ordering food delivery, and delaying major purchases. According to a Coresight Research survey, 47.4% of consumers plan on keeping these new habits in the long term. So, how do marketers learn what new habits their customers have picked up and stay on top of consumer behavior trends? They’ll need to invest in customer data analytics.

Customer analytics is the analysis of customer data to make better business decisions. Companies who use customer analytics have seen significant results. A McKinsey survey indicates that customer analytics improves overall company performance including ROI, sales, profit, and KPIs, which allows them to outperform their competition. There are three different types of customer analytics: descriptive analytics, predictive analytics, and perspective analytics. When choosing which type of customer analysis to conduct, it’s important to consider the company’s customer strategy goals.

Before getting started, marketers need to understand the goals of their customer data analysis. Once goals are determined, then the team must identify what type of customer data to analyze. Data collected from surveys and census data doesn’t provide a full picture of who customers are and their preferences. By enriching customer data with location intelligence, businesses can get insight into the new customer journey.

Customer segmentation is one of the most effective ways to ensure businesses are reaching the right customers. By dividing customers into segments based on location, marketers can tailor the messaging of their campaigns towards the region, city, or town, known as marketing localization. Marketing localization allows companies to increase their brand awareness and create a better customer experience. Localization isn’t just about adjusting the messaging language; it also includes understanding what products or services appeal to consumers living within a particular location. Take, for example, a resort specializing in outdoor activities that wants to run two separate marketing campaigns: weekend getaway deals for local residents and out-of-state tourists living in the adjacent state. The resort can segment out their customer data into those two different groups based on location, increasing the chances of reaching potential customers who would be interested in those deals.

Insights from location intelligence can also be used to inform personalization. Our hypothetical resort, for example, found value in segmenting customers by region, but they also need to consider personalization at the individual level with advanced audience targeting. Do their local customers often visit sports shops or retailers who specialize in camping equipment? Are their customers who live in the adjacent state more likely to go to spas or salons? Based on this information, the resort’s marketing team is able to target their local campaign to consumers who visit local camping or sports shops rather than spas or salons.

Customer data analytics is vital for marketing teams looking to keep up with the emerging customer trends. When analysts enrich customer data with location intelligence, they are able to correlate foot traffic data with sales or other trends to see changes over time. This insight allows marketers to know how customer behavior has changed and adapt their products or services to those trends. In the case of the resort, the marketing team can use insights from location intelligence to see how their customer behavior has changed. They might find that their out-of-town customers used to frequently go to salons or spas, but now they are visiting beauty supply stores more often. This can indicate to the resort that their out-of-state customers are now taking their spa days in the comfort of their own homes. They can adjust the campaign messaging to let those customers know that they get a special in-room spa kit as part of their vacation package.

A customer data analysis done with location intelligence allows businesses to compare differences in consumer behavior at their own locations (or even their competition’s locations). Foot traffic patterns can be compared to see which locations are getting the most business or how long customers spend time at each location (also known as dwell time). Customer data can also be enriched with buyer personas.

Let’s say the resort has two different locations and is looking to understand where customers go before their stay in order to inform their room services. They might observe that customers who stay at resort A are more likely to go to speciality grocery stores than those who stay at resort B. This allows the marketing team to know that they should consider partnering with the speciality store to provide their guests with contactless grocery delivery.

Consumer data enrichment is worth investing in, especially in the age of digital adoption and transformation. Predicting customer behavior has become an immense challenge in a post-pandemic world, but quality customer data can provide analysts with the information they need to predict future trends. With location intelligence, businesses can understand their customers better and improve the customer experience.