Predictive analysis is the use of Big Data and machine intelligence to predict the likelihood of a specific outcome, such as a trial user becoming a paying customer. Marketers can theoretically apply those predictive insights to support lead generation and sales opportunities. However, to create campaigns that are measurable, marketers need insights that are actionable.
By homing in on small data, predictive insights become more actionable and practical. In other words, small data takes Big Data from theory to practice.
Is smaller really better?
Bigger isn't always better, especially in predictive analysis. It's a great starting point, but it can also be overwhelming and unmanageable.
Small data has been described as subsets or slices of Big Data, or the nuggets of actionable data resulting from the analysis of Big Data. Small data can also include the often overlooked customer detail typically found in unstructured data—such as in phone calls, emails, text chats, and the like.
Though the definitions of small data vary, they all hit on one important point: data must be small enough to be actionable, or it's not useful.
Moreover, by using Big Data, marketers can gain a broad view of their target audiences.
For example, take small and midsize business owners and sales managers. Through considerable analysis, data about those prospects becomes smaller and more specific: SMBs or sales managers in the financial services, creative services, or real estate industries with at least two employees. From there, marketers can begin to develop messaging and campaigns to address the challenges faced by that audience.
Over time, marketers and sales staff gain additional insights based on their outreach and conversations with prospects and customers.
By linking the small data found in unstructured interactions with specific outcomes, such as upsells or renewals, marketers can identify nuanced messaging or customer attributes they should focus on to increase those outcomes.
How small data complements predictive analytics
Small data really shines in regards to analyzing usage patterns.
For example, a software provider can use predictive analytics to identify specific engagement markers, such as how often a user logs in or performs a specific function, or which features are used most. Those markers can also surface opportunities, such as when customers are ready for an upsell or when they need help to move forward.
When marketers know what they're looking for, they can use predictive analysis to better calculate an outcome.
For example, if a company wants to know the likelihood that its trial software users will become paying customers, its marketing team can compare the behavior of testers to that of paying customers. With that information, marketing can send personalized emails to those users identified as more likely to convert and offer valuable information, such as an e-book or whitepaper. Doing so is not an exact science, of course, but by focusing your efforts on those prospects that share similar traits or behaviors as your best customers, your conversion forecasts will be much higher.
The role of small data in the customer experience
Conversations between customers or prospects and your team members are likely to reveal specific details and actions that may not be evident in Big Data. For example, customers may react more positively to specific messages than to others, but if those messages are delivered in text chat or on the phone, they won't be included in a Big Data set. That small, unstructured data can be valuable when paired with predictive analysis.
Small data can play a big role in the customer experience from the start of the customer journey. By identifying the specific engagement markers exhibited by established customers, companies can create more effective onboarding processes.
Say a company knows its most successful customers log into the tool daily for the first two weeks, but a new user is not exhibiting that behavior. The customer success team can reach out to offer assistance.
Predictive analytics can flag markers at specific intervals, such as by day five, day 12, month three, etc. This data shows what successful customers are doing in terms of engagement, but it can also reveal "delight metrics" exhibited by power users. With this information, marketing, sales and customer success teams can better understand and meet the needs of their users.
Small data for the win
Data is only as good as the action it enables. Small data is where the action lives. When evaluated with predictive analysis, that small data can help marketers refine lead generation, support sales teams, and empower customer success specialists in delighting their customers.
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