Leveraging Predictive Analytics Begins with Deciphering the Data

[vc_row][vc_column][vc_column_text css=".vc_custom_1509024875727{margin-bottom: 0px !important;}"]Sometimes all that marketing data can be a bit too much. Reviewing, extrapolating, and enriching data can quickly become a cumbersome, confusing, and time-consuming affair. Finding a happy medium between too much data and not enough comes down to identifying the type of data that matters most to your enterprise. This trek is by no means easy. Spend too much time mired in your marketing data and your predictive analytics model will suffer. Finding a balance is key.

Yes, quantity matters. You want as many reference points as possible. However, you want that data to mean something. Data quality is far more important than the quantity of data. Once you have identified good data and discarded the rest, you want a martech stack that continually feeds your predictive analytics model with as many data points as possible.

Your goal moving forward should be to identify the most pertinent marketing data and then increase the amount of that data you gather. Quality first and then quantity. So, what must you do to ensure that you are deciphering the right data so that your predictive analytics model is firing on all cylinders? Better yet, how do you make sure you are gathering more of the marketing data that matters and not allowing your assumptions to lead you astray?

1. Leave Your Assumptions at the Door

Far too many digital marketing teams use assumptions when reviewing their data. They want their data to validate these assumptions. They want their data to confirm long-held beliefs and reassure them that they are on the right track and adopting the right strategies. They tend to place greater emphasis and weight on data that confirms their assumptions. Confirmation bias is a constant issue for digital marketing professionals and one you must eliminate.

Make sure you are deep diving into your marketing data. Do not try to fit that marketing data within a world where your time-tested strategies make sense. Instead, ask yourself what the data means. Ditch your assumptions. Go into your analysis with a clean slate and always question data clusters without any preconceived notions. Ignore your long-held beliefs and allow the data to create a new hypothesis. What is this data telling you and where is it leading you?

2. Improving Top-of-the-Funnel Data Improves Bottom-of-Funnel Results

Eliminate the "garbage in - garbage out" issues that typically plagued digital marketing teams with their database management software. This all feeds back to your martech stack. Knowing which technology your customers prefer is essential to collecting the right data at the right time. The more applicable your data at the top-of-the-funnel, the better your bottom-of-the-funnel results. A periodic review of the quality of your data must start with you questioning whether your martech stack is up to par and whether you have properly identified the type of technology your customers prefer.

3. Clearer Definition of Buyer Personas 

Improving the quality of your data and increasing the amount of data you gather will improve your understanding of your buyer personas. It will either confirm long-held views of your buyer personas or put you on a path to redefine those personas. It’s not about validating what you already know but more about letting your data lead you to new conclusions.

Factual assertions only come from marketing data that is continually enriched. The more you enrich your data the more you will understand your buyer personas. Data quality is key. Again, assumptions have no place here. Allow your marketing data to guide you instead of trying to force the data to fit what you already know.

4. Upgrade in Martech Stack

Upgrading your martech stack must become a constant pursuit. Once you have clarified the type of data you need, your next step is to expand the technology platforms available to your customers. The goal is to expand your data reach. Understanding the specific data that increases customer engagement only happens when your technology platforms are up to par.

Predictive analytics
The most important data leads you to a closer customer relationship.

5. Dollarize Your Data

There is nothing worse that proclaiming to have a great brand without the revenues to go with it. Yes, your online reputation matters, and yes, increasing customer engagement and building your brand matters. However, if none of that leads to increased revenue, then why bother? Ultimately, your marketing data must mean something to your bottom line. Follow the money. Understand which data leads to increased revenue and which data is nothing more than noise.

6. Continuous Process

Improving data quality is never a one-time event. Data enrichment is a full-time pursuit. It is a never-ending process where you continually improve data quality by improving how you gather that data and what you do with it. First, improve your predictive analytics model by identifying your most important data. Second, increase the amount of important data you gather by improving your martech stack. Third, follow the data and allow it to better define your buyer personas. Fourth, dollarize your data by following the money. Fifth, repeat this entire process and enrich your data even further. Finally, never allow assumptions to guide you. Let the data guide you.

7. Another Set of Eyes

You may have a team of tech-savvy digital marketing professionals whose predictive analytics model is perfectly tuned. You might have excellent data quality and the perfect martech stack. However, there is still value in having another opinion. If you feel that something is amiss, or if you feel that your model is almost too perfect, then get another opinion. A third-party marketing firm might just be able to shed some light on data you have overlooked.

Data quality begins and ends with how well your team reaches its audience. Improving data quality from the outset improves your overall results but only if you are constantly enriching that data and not allowing your assumptions to guide your conclusions.

ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization, and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

To learn more about how ReachForce can help you optimize demand generation and your impact on revenue, get a free data assessment and get a demo today.


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