Good data collection is essential when boosting your customer retention efforts. It helps improve customer agents’ efficiency and helps to offer a personalized service. However, software and hardware solutions often involve large volumes of data. Often, knowing the specific data types to focus on can be challenging. Here are some customer data examples you should be collecting.
Contact Information
Customer service has a general rule. The more you know about your customers and their demographic details, the better your chances of satisfying them. Keeping an updated list of contacts is a must. Gathering customer phone numbers and other contact information is a great way to ensure CRM success. And depending on your marketing strategy, you can generate customer contact information in many ways.
However, using digital solutions like predictive dialer software may be easier. Convincing customers to take contact recording surveys can be hectic, but they’ll likely prefer survey options that don’t take much time or demand too much information. Virtual systems don’t waste time with awkward pauses in between surveys.
Buying Behavior
Customers buy products or patronize services for many different reasons, including your cost or exclusivity. Understanding what influences your customer decisions can take your sales and CRM efforts to the next level. These insights can also influence your marketing strategy to attract new customers or retain existing ones. For example, if you’re an e-commerce retailer, you can study time zones and the relationship between different seasons and your sales numbers. You can also check your website for the factors triggering web traffic and keep tabs on such factors.
Using platforms like TaskMonk to leverage statistical algorithms and data-labeling features for better customer data management can also be a great option. Taskmonk favors continuous data optimization, and the platform integrates all data types from product taxonomies to attribute lists. That way, businesses can use multiple customer data points to train artificial intelligence applications, which can help small businesses in many ways. The most important is the automation features, which help repetitive field tasks more quickly. This is one of the main reasons businesses keep adopting AI applications.
Business Discovery
About 81 percent of modern customers research a business before patronizing a business’s services or products. They can search via Google, social media, or even by asking a friend. Knowing how consumers find your business can be a good indicator of your sales and business goals. However, business discovery data analytics can’t be complete without understanding why customers choose you.
After finding your startup, customers still need that push to make the final call. Knowing why a customer chooses to do business with you is a great way to repeat sales and reduce bounce rates. You can leverage those insights in your customer engagement strategies, from ad copies to social media campaigns. You can also use these insights to understand why customers leave your business for competitors. Wrongly assuming what causes them to jump ship is not good business practice. Therefore, it’s essential to go full circle in recording customer attraction and detraction data.
Net Promoter Score (NPS)
Net promoter score can be a very revealing metric for happy customers, as it indicates how customers are likely to recommend your businesses to other prospective patrons. Determining your business’s net promoter score can contribute to your word-of-mouth marketing efforts, which have become essential now more than ever, especially if you’re a small business. As the business world becomes saturated with increasing competition levels, prospective customers trust reviews and testimonials more than fancy business-led ad campaigns.
In conclusion, it pays to know that selecting customer data to focus on is not an either-or situation. You can be collecting multiple customer data types based on your business needs to guarantee customer satisfaction.
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