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Actian Blog / Understand Customer Buying Behavior To Improve Customer Targeting

Understand Customer Buying Behavior To Improve Customer Targeting

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Imagine what you could do if you actually understood your customers’ buying behavior. You could predict future purchases, identify cross-sell and upsell opportunities and personalize messaging that would resonate and drive the behavior you desire. Instead of taking broad strokes to your marketing campaigns, you could focus on specific opportunities and pursue them more aggressively. This would be a game changer for your marketing function.

Your target audience is too big

Most companies today are doing market segmentation based on basic account information and demographics. They are looking for groups of customers based on high-level account-and-behavior metrics that are designed to identify similarities, not differences. The results are a grouping of customers that fit into some conceptual “bucket,” but not necessarily a set of high-potential leads.

This is because it is each customer’s unique challenges that lead to highly motivated buying behavior, not general characteristics. For example, a grouping might be “females living in a certain geographical region with no children and an income between $50k and $100k/year.” The metrics used for targeting are accurate, but they may not be meaningful to identify customers likely to make a purchase of the product you are offering during the next 3–6 months.

The marketing campaign you develop based on the group demographics are likely to be both expensive (due to the size of the target audience) and generate poor results (because the audience is too broad). You are effectively gambling with your marketing investment, hoping to be lucky and see a return.

Focus on specific customer needs

True customer engagement is built on a deep understanding of specific needs and wants, which then leads to more satisfied customers and longer lasting relationships, increasing revenue and wallet share for your business. If you can narrow your target audience by analyzing a richer and more diverse set of reference data, then you will be able to focus on a smaller target-market segment that is more likely to make a purchase.

With a smaller and more focused audience, you can create a better customer experience with targeted offers, appropriate responses and effective dialogue.

Using data to understand why customers behave the way they do

The key to understanding customer buying behavior is analyzing cause-and-effect relationships to determine what situations, characteristics or influences lead a customer to perform a specific action – statisticians refer to this as correlation analysis. Effective correlation analysis requires a diverse dataset and is most effective with a large amount of historical data.

Unfortunately, most companies don’t have the right systems to process large volumes of historical data and diverse data sets, so they instead work with samples. While sampling techniques work fine to identify averages and commonalities, you actually need to process the entire population of data for effective correlation analysis and to understand the causes of behaviors. This is where Actian Vector can help.

Better tools lead to better results

With Actian, you can connect and mine all your data, including big data sources, to obtain a detailed, holistic view of the customer. By including not just their actions (sales history), but also contextual data, such as individual profile demographics, social influencing networks, location data and sentiment (reviews of products/services), Actian can help you uncover relationships between customers and key purchase drivers.

You can use this information to predict the value of each customer according to thousands of customer attributes. You can uncover new segments that your competition has overlooked and you can engage in more meaningful customer conversions that generate higher returns on your marketing investment. To learn more, visit www.actian.com/vector.

About Pradeep Bhanot

Product Marketing professional, author, father and photographer. Born in Kenya. Lived in England through disco, punk and new romance eras. Moved to California just in time for grunge. Worked with Oracle databases at Oracle Corporation for 13 years. Database Administration for mainframe IBM DB2 and its predecessor SQL/DS at British Telecom and Watson Wyatt. Worked with IBM VSAM at CA Technologies and Serena Software. Microsoft SQL Server powered solutions from 1E and BDNA.