For years, mortgage lenders and B2B companies in the industry have used the same data to predict whether or not a prospect may be in the market for their services. Lenders typically use pre-screened credit, home/mortgage, income, trigger inquiries and other data that can be purchased from any number of list providers as predictors for the purchase or refinance of a home.
Servicers primarily focus on interest rates and loan-to-value ratios to predict mortgagor behavior. B2B companies (document technology, appraisal firms, loan origination systems providers, etc.) rely upon email tracking data, website page visits, bounce rates and clickstream analysis as predictors of future purchases.
The fact is we're quickly approaching a time when these "old-school" approaches to customer acquisition will be greatly enhanced by much larger sets of predictive data. This data will ever more accurately target those most likely to be in the market for a new home, refinancing or a new vendor of other industry services. Enter "big data."
So, you may be wondering "what is all the buzz around big data, how do I access it and use it to increase my marketing effectiveness and return on investment?"
First, let's define "big data." Big data refers to the vast amounts of raw data being made available to marketers as a result of how much our individual lives have been digitized. Think about this: every time you click on anything on the Internet, it's recorded. Your location is recorded. Every search is recorded. Every time you post something on Facebook, Twitter or any social venue, it gets recorded. If all of this data on individuals can be properly analyzed, it can predict the likelihood of an individual to purchase anything from a new pair of athletic shoes to a new home or for a mortgage company to purchase your secondary marketing technology or any other service.
Sounds great doesn't it? Well, not so fast.
Here's where the challenge occurs. How do you analyze all of the millions of pieces of data available to determine which pieces are reliable predictors of an individual to obtain mortgage financing? Or how about for a mortgage company to purchase any number of services or technologies?
For example, maybe upon analysis you determine that a large number of people purchase homes in the spring and summer months. Maybe you also find that a substantial subset of these people are purchasing barbeque supplies during these months as well. So you could conclude that home sales are correlated to barbeque supply purchases. However, only a very ill-informed marketer would use the purchase of barbeque supplies as a predictor for the purchase of a new home. The only way these two pieces of data are correlated is that they both occur when the weather is warmer. There is no causal relationship. However, data which suggests that a wedding is underway could very well indicate that a new home purchase is on the horizon as well. See the difference?
The point here is that without proper analysis, it is easy for the mind to get "tripped up" and mistake correlation for causation, similar to how an optical illusion tricks your mind into seeing something that doesn't exist.
Therefore, access to better data as an overlay on traditional data selects can further hone campaigns, bringing a variety of benefits to both your brand and marketing efforts.
Here are six ways you can use big data that will drive a higher marketing ROI:
1) Use it to elevate the perception and positioning of your brand. Better data leads to better, more targeted, more personalized and better timed campaigns and communications. It helps you target the right individual at the right time with the right message. So what does this tell the prospect about your brand? It implies a better experience due to a better understanding of their needs and superior technology. Millennials will take note of your technological prowess; they expect to be treated as individuals.
2) Better segmentation of your target audience. Up to this day, many lenders develop campaigns based on broad demographic and credit-driven data, or explicit data, using one-size-fits-all messaging. As we move forward, large vats of data garnered from your digital life including implicit data will be analyzed to allow marketers to make their campaigns more personalized and targeted which will increase conversion.
3) Improved optimization of your marketing mix. With big data, mortgage lenders and other industry service/technology providers can improve how they allocate marketing dollars for higher ROI. Marketers can determine how their target audiences like to be communicated with down to the individual level. For example, some people prefer to be communicated with via email, while others have a tendency toward watching videos while yet others may be more responsive to targeted ads. Big data allows marketers to analyze their spending and determine where to invest more or less time and money.
4) More effective content optimization. Before big data came into play, it was nearly impossible to track the effects of your content marketing activities and how well they pushed prospects down the sales funnel. This has been a cause of friction between chief marketing officers and CEOs for years as it has been nearly impossible to quantify the value of a tweet, Facebook post, YouTube video, etc. in driving conversions.
Now, you can analyze the effectiveness of your content down to a single tweet in driving new qualified leads. This allows marketers to better understand what content works and what doesn't by scoring the content based upon overall effectiveness.
5) Optimizing the UX (user experience). There is plenty of data that, upon proper analysis, can provide great insight into how your audience interacts with your brand online. This analysis can be used to improve their experience with your brand effectively driving greater conversion. This improved experience has a direct effect on the perception of your brand by the user and what it would be like to work with you through implementing a new LOS platform for example or, for homebuyers/refinancers, the mortgage process.
6) Stronger campaign modeling capabilities. Mortgage companies can analyze a group of closed mortgage leads by product (30-year fixed, reverse, non-QM, etc.) and review historical internet behavioral data to develop more precise models for targeted campaigns. Similarly, B2B companies can do the very same with their successfully closed leads as well.
Data aggregation is going to a whole new level in the coming years. It will create a seismic change in how we approach the marketing function, allowing for more targeted campaigns that drive a much higher ROI.
Get ready for some real excitement!