For many small businesses, “Big Data” isn’t ever a consideration. However, Big Data isn’t just for big businesses as current technology makes data-driven marketing realistic for all businesses, regardless of size.
Want to take your company’s sales prospecting efforts to new heights, lowering cost per acquisition and increase your conversion quotient significantly? Of course you do. Toward these ends, here are 5 great reasons to leverage the power of predictive analytics to get more—and higher quality—customers and increase your bottom line:
1. Greatly Amplify Prospecting Efforts.
Companies can maximize their email-based prospecting with better tracking, measurability and funneling to more efficiently and cost-effectively target viable prospects and convert them to actual customers. For example, most companies may have multiple links in their emails but they also have the same follow up sequence whether a potential customer clicks a link or not. To properly measure for potential new customers, a company should put all prospects that click on a link in an email into a completely differently follow-up sequence. This automatically separates the lookers from potential buyers simply because they clicked on a link. That says they take some type of immediate action when something is interesting to them. Instead of marketing to the masses with hopes of a small conversation, now you have a targeted list of prospects that want exactly what you’re selling and your conversion rates will be higher by default.
2. Readily Vet High Value Customers While Avoiding ‘Bad Seeds’.
Not all customers are created equal. Focus on potential customers who are the best fit for your business. This might mean turning down some customers but in the long run it’s worth it because those who remain are 100% satisfied customers because they love working with you.
3. Boost Conversions by Using Small Data to Better Utilize Big Data.
When you start small, you have the chance to start fresh and gather meaningful data at the very beginning and build from there. In a few months when you’ve built up even more data, that data becomes increasingly accurate and requires much less maintenance. Let’s not forget that big mammoth companies are less likely capitalize on the latest trends as quickly. This gives the small business a chance to grab up that data and get new customers in the process. Eventually, you’ll be able to turn small data into very valuable big data and that data turns into high profits and better customer relationships.
4. Leverage the Power of Behavioral Economics for Customer-Centric Prospecting (vs. en masse).
Sometimes big data has lots of variables. Behavioral economics isolates a single element to experiment with. By focusing on what items your high value customers purchase and what funnels they take that convert them into buyers for those items, it’s easier to test specific sections of those funnels one at a time to see if they can be improved instead of gathering massive amounts of data at once and not being able to translate that into buyers.
When it comes to Big Data, your strategy needs to be customer-centric, otherwise you are just collecting data to collect it, and hoarding information won’t earn you trust, respect or ROI. You can’t guess what will work. You have to find users and measure their responses. A company doesn’t have to spend a significant amount of already limited resources to acquire new customers if they focus on the right components.
5. Combine Data Sources to Accelerate New Customer Acquisition.
Not having a specific user in mind can be a critical mistake and hinder the ability for a company to acquire new customers. Businesses now have the power to craft customized messages by gaining access to valuable customer insights. This will win new customers and stay ahead of competitors. Not only can you get access to traditional sources like transaction history or demographics, but public social media behavior is now available. By combining these data sources, small businesses have the ability to create more personalized and effective customer acquisition campaigns. For example, if a prospective customer made a number of purchases at Publix and “liked” the Food Network on Facebook, a company could send a specific offer with a special promotion related to Publix or the Food Network. Analyzing data from just one source isn’t good enough anymore. In order for companies to create campaigns around personalized promotions, a company must analyze traditional and emerging data sources together to lower their customer acquisition costs.
For any sales-driven organization, it isn’t the size of your data that matters, it’s what you do with it. No longer a discretionary luxury, predictive analytics are now the name of the game for those who seek to utilize customer metrics in a meaningful way to establish a tremendous competitive advantage, gain notable market share and significantly boost bottom lines.
