You may not even realize it, but there’s a good chance you’re sitting on mountains of insightful and in-depth data about your customers, which you could use to grow your business efficiently. Even better? It’s easy to interpret this data, thanks to a marketing technique called data mining, which can help you attract more customers and make your business more profitable.
What is Data Mining?
Basically, it’s crunching the hard data of your customers’ attributes – everything from their buying behavior and age to their credit scores and income levels. Poring over this information can reveal specific patterns about your customers that you can use to sell to them more effectively than ever.
There are all sorts of ways to discover this information because you’re already sitting on it. Each time customers make a purchase, fill out a survey, and spend time on your website, they’re willingly giving you amazing insights about:
- Their preferences
- How they like to shop
- When they like to shop
- How much they’re willing to spend
- How near they live to your location(s)
- Conversion rates relating to incentives
How to Data-Mine
There are numerous ways to use your customers’ data to find out more about them. You can create and expand a customer database by inputting information you collect from:
- Sales
- Surveys
- Subscriptions
- Contests
- Email newsletters
Another data-mining tactic involves improving customer loyalty by directly asking your customers what they think your brand can do better. The more you engage your customers like this, the more they feel your brand is personable.
Talk to your customers to get this rich data by:
- Engaging them on social media by asking them questions and responding to their feedback
- Asking them to fill out online questionnaires that address interaction with your brand
- Hold focus groups where you can sit customers down and have face-to-face interviews with them
You can also talk directly to your salespeople and marketers to ask them how they think your brand can increase customer engagement and feedback.
All the data you get from this technique will help inform your future customer interactions and how you sell to them. You can then use this information to:
- Run promotions on only the days your customers are most active
- Upsell and/or cross-sell to your customers with relevant offers
- Tailor future contests more to your customers’ preferences
- Provide better customer service to create happier (and more repeat) customers
The possibilities are almost endless.
How Data Mining Boosts Revenue
What makes techniques like this truly powerful is seeing how businesses successfully use them in real life to earn more money.
CNN ran a story that focused on two small businesses: Sway, a women’s retailer, and Grasshopper, a virtual phone system provider. Both used data mining to boost profits. Sway saw a 300% increase in online revenue, while Grasshopper experienced a greater than 25% reduction in customer attrition (resulting in yearly advertising savings of $100,000).
They both relied on predictive-analytics software to mine their customer data. This allowed them to understand customer behavior patterns to where they were able to send them more personalized email-marketing campaigns and simplify online transactions – much to the delight of their customers.
Crunch Those Numbers
It may not be the most exciting activity when running a business, but data mining is absolutely necessary in order to increase your customer base and revenue. It can take the form of rote activities like building your own database from customer data you get through purchases, or it can come from predictive-analysis software like Retention Science.
However you do it, it’ll be instrumental no matter what business you’re running –whether you’re offering social media marketing services or providing online lessons.
How well do you know your customers? Have you ever looked at the data they provide you with each interaction? Let us know in the comments!
The post How Mastering Data Mining Can Get You More Customers and Revenue appeared first on .
