From optimizing research and development to streamlining production and delivery, Big Data is transforming a whole host of business functions. But, perhaps the most important utilization of Big Data is concerned less with processes and more with relationships.
That’s right; your customer service and sales teams could be the biggest users of Big Data. Why? Because it provides unprecedented insight into what customers want – whether that’s how they want to learn about your product or how they want to engage with your brand when they contact you.
It’s true that data is the new competitive advantage, and utilizing business intelligence software that aggregates and analyzes Big Data can give your customer-facing employees the insight they need to interact with customers in a true customer-centric fashion, rather than following a rigid script.
The road to harnessing Big Data begins with analyzing all of your business’s data sources. For your sales team, this will probably start with their customer relationship management (CRM) software. A customer service team may have a broader number of data sources, from a CRM platform to website data that includes purchase history and shipping.
Here are three ways to use the data you probably already have, or could easily and ethically collect, for increased sales and better business intelligence and insight.
Personalize Your Customer Touch Points
For saturated markets like telecommunications and banking, finding new customers can be difficult. While acquisition still occurs, most of the consumers in the market have a service provider. This makes every customer interaction vital, because any negative experience with your brand could send them running in the other direction.
All businesses benefit from a greater focus on customers, and Big Data has a huge role to play in building customer-centric relationships, mainly by helping customer service reps to personalize their interactions with consumers.
Consumers are increasingly jaded about customer service because of the horrendous experiences they’ve had in the past (often with cable and internet service providers). But personalizing customer interactions isn’t as complex as it sounds. Exceeding customer expectations in this realm often only requires quick, accurate access to previous information and a dedication to providing a solution.
As a real-world example, Zappos consistently wows customers by combining a customer-centric philosophy with real-time reporting software. When you call or email Zappos, the customer service rep you talk to knows every detail of your history with the company. He or she won’t ask for redundant information or try to up-sell you when you’re calling to resolve a problem. This builds brand trust by improving the customer experience and using customer data to help the customer, not just the brand.
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.
Construct a Holistic Buyer Journey
The intersection of customer needs with a product or service your organization offers is the foundation for good business. In this mission, Big Data has a role to play in helping you understand what exactly your customers want, as well as when best to engage them about said service.
The most common reason marketing communications fail is the misalignment of messaging and customer position in the buying journey. The end goal for marketers, then, should be to establish a holistic understanding of customers based on data from online behavior that isn’t tied only to an employee’s brand. Data from your brand will only provide a limited view of your customers. Utilizing social media analytics to gain an understanding of how your customers feel will allow you to fill in the qualitative gaps in their profiles.
Let’s take HubSpot for example. The company analyzed the path visitors took to their website and found that their visitor-to-lead conversion rate from LinkedIn was 277% higher than both Facebook and Twitter. \
For HubSpot, the obvious takeaway was that LinkedIn users were much more interested in Hubspot’s marketing automation software than those on Facebook or Twitter. For the company, this analysis shifted the ad spend from a focus on Facebook and Twitter to a focus on LinkedIn, with maintenance only on other platforms that provide brand recognition, but not really heavy on the qualified leads.
This shift in ad spend based on behavioral data is great, but it is only one piece of the puzzle. To gather a truly holistic understanding of the customer, and increase qualified leads from your most successful platforms as well as maintenance platforms, complement on-site behavioral data with psychographic second-party data collected via social authentication.
By doing this, you can increase your customer understanding and find previously unknown customer adjacencies and affinities. These new data points can then inform your copy, creative and more.
The point? Don’t just use big data to identify where your potential customers are, use it also to craft meaningful off-the-brand-site experiences that bring people back to your content and into your sales funnel time and time again.
Forecast with Greater Accuracy
Predictive analytics was one of the first use cases to truly propel Big Data into the minds – and budgets – of many businesses, and it remains one of the most compelling ways to gain a competitive advantage when using data.
This final strategy may take a bit longer to develop, because in order to accurately perform the necessary statistical calculations, a significant amount of historical data is required. However, once said data is gathered and implemented, businesses have a powerful tool for predicting a range of customer behavior or business projects.
The major benefit of prediction is, of course, to make more informed decisions in the present.
Notable examples of predictive analytics include churn modeling, which predicts the likelihood that a current customer will change service providers. Churn modeling has been used by financial institutions and telecom companies for some time, though it’s being adopted quickly by Software as a Service companies.
Building a churn model can help you forecast how your customers will react to changes in service offerings or products, and allow you to build out a predictive analysis in which you can test large-scale changes in certain regions before implementing them across your entire customer base. This will help to ease the process of A/B testing, while also gathering enough data to better understand how changes should rollout to your customer base without increasing loss of clients in the process.
Using Big Data for business intelligence purposes doesn’t have to be a complex process. In fact, many businesses already have data they can reference to improve the customer experience, particularly in customer facing job functions. The first step is to isolate a business problem and then search your existing data for answers. For your sales team, both real-time and predictive analysis can up the ante on successful sales forecasts and overall closings.