But here’s the catch when it comes to effectively incorporating big data analytics into your marketing strategy: it cannot give you the answers you want unless you ask it the right questions. And asking the right questions of big data is the only way to get measurable results.
However, you shouldn’t feel like your business is the only one slow to squeeze answers out of your data: a recent Oracle study indicates companies are not really using the big data they have spent so much time collecting. Your data represents an extremely valuable business asset, and if you aren’t using it, you’re wasting it.
Why/When Are Customers Leaving Us?
While different stakeholders within your business or organization will usually have different answers to this perennially important and painful question, data can provide you with a non-partisan answer.
When T-Mobile set out to decrease its churn rates, it looked to big data to tell the story of customers who jumped ship and went to another company. The company noticed that customers who left shared similar behaviors across billing cycles, web logs, and social media channels.
Having identified several markers as risk factors, the company was able to effectively intervene when some of these markers popped up, decreasing churn rate within a quarter.
How can you do this? Find the customers who left your company and backtrack their behavior for the previous six months. Look at factors like number of customer complaints, late payment, poor customer service experiences, length of time with your company, answers to customer surveys, etc. You can then see patterns customers follow before they leave you. Next, create a system to flag at-risk customers (and, of course, intervene when they’re flagged).
How Effective Is Our Social Marketing?
With companies investing so much time and money into social marketing, it’s crucial to know what works and what flops with your particular audience. How many tweets are too many? Are your Facebook fans turning into paying customers?
ThinkGeek successfully tracked its social metrics and compiled all its data to discover the best frequency, time of day, day of the week, and kind of content for posts on all its social channels. The company then used this data to tailor frequency and content to its users’ behavior. The results? ThinkGeek now drives 48 percent of its revenue from its social channels.
How can you do this? Start tracking mentions, comments, replies, clicks, and image views to measure engagement rates and traffic peaks (this will vary by social channel). Also, track site visits, page views, orders, and revenue that come from your social channels in order to track conversion and revenue. Then, just like ThinkGeek, tailor your content to your users’ behavior by posting, retweeting, repinning, etc. at peak hours and at the frequency preferred by your followers.
What Do Our Customers Actually Want?
Finding the commonality in what your customers like can be eye-opening and can prevent you from making big investments in products or services that no one is interested in…or from passing up on something you should have pounced on immediately.
Netflix proved this point when it optioned the wildly successful series “House of Cards.”
Execs weren’t originally sure whether to move on the series or not. Sure, it was good, but would Netflix audiences like it? Luckily, Netflix had more than enough data at its disposal to help make a pretty educated guess. Do viewers like Kevin Spacey? Yes. Do they watch gripping, cynical political dramas? Yes. Do viewers watch material like this all the way through? Yes! Then, does it follow that they will like this series? Yes! And so it is that “House of Cards” fans have big data to thank for hours of enjoyable TV watching.
How can you do this? If you don’t already have customer feedback on which products they do/don’t like, then send out surveys asking which products have been their favorites. A much more targeted approach is to send out surveys directly after a customer received a product/service and ask what she liked or didn’t like and what she would like to see more of. You can then use this data to track customer preferences and predict what will or won’t go over well in the future.
With so much invested in big data analytics, small business owners and marketers are on the right track when they decide to use a targeted and intentional approach to analyzing their data. But remember, while there may be good and mediocre questions, the worst mistake you can make is being too afraid to ask your data any questions at all. The process may be challenging, but it can create an incredibly targeted and effective strategy.
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