Before the rise of the social web, sales and marketing departments were forced to rely on historical sales figures to try to predict how and when customers would pull the purchasing trigger.
Today, customer intent clues are widely available via comments on social networks, previous browsing behavior, email open rates, call center and sales department interactions and past receptiveness to offers and promotions.
But this big data gold mine is spread across multiple channels that often fall outside a company’s internal network landscape. And that means determining customer intent requires data analysis to mine for the insight that can bolster sales.
To predict customer intent, organizations need to embrace predictive analytics to gain actionable insight from the rapidly growing number of data sources flush with customer data, notes Paul Oram, chief technology consultant of consulting company Fresh Networks.
In additional to enterprise data sources, Oram advises organizations to capture and analyze data including:
- the mobile data consumers generate on smartphones and apps
- data on how customers search
- data from sensors and meters in homes, vehicles or other places
- social data where customer intent is revealed through comments, likes, connections, plans and thoughts
For example, Oram notes that Wonga, which provides automated, same-day loans to consumers, has used data analysis to determine that traditional credit scoring was not useful for its target customer base. Instead, the firm applies data analysis to survey multiple data sources including social media to make real-time credit assessments. As a result, the past year’s profits have tripled.
Moreover, there is a four-stage life cycle for how customers interact with companies, according to new research from Forrester Research. Customers discover new products or services; they explore them in more detail; they buy; and then they engage with the company and its other customers, notes Nate Elliott, vice president and principal analyst at Forrester. Each lifecycle involves different channels.
“Most customers tell us that when they’re open to discovering new products and services, they rely on mass-reach channels such as TV ads, search engines and word of mouth,” according to Elliott. “When they want to explore products in more detail, they use depth channels like marketers’ websites and retail stores. (They turn to these same channels when completing purchases.) When they want to engage with their favorite brands, they use relationship channels – signing up for email lists or loyalty programs, or liking a brand on Facebook.”
To pinpoint customer intent, companies must go beyond the “last mile” data contained in transactional records and instead use analytics to delve into the customer journey, according to a recent ClickZ post.
Here are four steps to identifying customer intent using data analysis:
- Identify where the customer journey begins. “Is it on the search engine or email message, or is it on the landing page? Often, the path to the source prior to the website visit is crucial to understand intent,” notes the ClickZ post. “Were they on a competitor’s site? Were they reading an industry article? Were they searching for free shipping offers?”
- Collect all data from customer actions. Capture data from kiosks, call centers, mobile apps and web data. Hone in on product views, shopping basket additions, comments, searches, video views and help requests.
- Study patterns. Determine which customers are reading or writing product reviews. When do they look at shipping information. What products are being compared or which product bundles are studied? After analytics uncovers patterns, companies can then tailor offers.
- Use data as research in lieu of expensive surveys. “Use your web data to assess what people actually do. Look for patterns in segments as well as feedback behavior – do reviews feature certain product characteristics? Highlight those in your marketing and up-selling,” according to ClickZ. “Web data can also improve your segmentation strategy, because it lets you segment on how (and potentially why) customers shop, not where they shop or who they are.”
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