In Big Data Marketing

Working as a marketer in today’s fast-paced, data-driven environment means you need to be ahead of the curve when it comes to new trends.

Competition is fierce for innovative ways to glean new information about your target audience. It is easier than ever to directly attribute success and ROI to specific campaigns and as a result, companies are investing more time and resources into doubling down on the methods proven to drive the highest conversion rates for new customers.

What does that mean for you?

It means pushing through to new frontiers in the world of marketing. The rise of the internet and subsequent emergence of big data marketing helped usher in a new era – labeled “digital marketing” – in the early 2000’s.

Now, in 2017, marketers are standing on the edge of another major development – a revolution even – in the way campaigns are formed and delivered.

Predictive analytics have changed the way marketers think about communicating with leads and existing customers alike. From promoting solutions based on target personas, to forecasting churn based on behavioral data, to even using big data marketing for sales planning purposes, a company’s ability to beat their competition relies heavily now on quality customer data.

“Quality” is the key term there. It is fairly simple today for marketers to put basic mechanisms in place to capture customer data; that does not mean it is high quality. Just as good data can catapult the success of your marketing campaigns to a new level, so can bad data tear it all to the ground.

Here is what it means to generate clean, quality data and the positive impact that can have on your entire marketing strategy.

Why Data Cleansing is So Important for Predictive Analytics

Picture yourself putting together a 1,000-piece puzzle without looking at the image on the box. You start by sorting out the edge pieces, then dividing everything by color. It has been an hour before you even start the assembly. Slowly, as you are building, you start to realize you are having a tough time finding enough pieces that actually fit.

But you do not stop.

Instead, you trudge along until every available piece is placed, and yet, at the end,  you are left with a gaping hole in the center of the puzzle so big, you are not even sure what the picture is supposed to be.

That is what happens with predictive analytics when you work with bad data.

You start out with what you believe to be a full dataset, only to find in the end that you have wasted time and resources putting together a puzzle you cannot even decipher.

Data cleansing

Predictive analytics without data cleansing is like putting together a puzzle with a ton of missing pieces.

How Does Data Cleansing Impact Predictive Analytics?

A previous post on the ReachForce blog detailed the influence data cleansing can have on your marketing campaign. Here are three key areas of predictive analytics directly impacted by scrubbing customer data:

  1. More Robust Target Personas. Improved customer segmentation is one of the major benefits of using predictive analytics for your marketing campaigns. By compiling firmographic and behavioral data in a quality data management system like ReachForce, you can create accurate customer profiles. Then, use those profiles to identify leads with the most potential and market to them in a way to which similar customers have responded in the past. Without clean data, however, you are likely to encounter false-positives. What looks like a trend may just be a reflection of your bad data; acting on that perceived trend could be costly to your budget and reputation.
  2. Better Lead Conversion Rates. At the end of the day, the job of a marketer is to drive leads to the sales team. Your success as a marketer is often based on conversion rates, so of course it is in your best interests to do everything you can to maximize those. Predictive analytics are helping marketers do that like never before. The truth is, however, you can run the best campaign in the world, but if the data is dirty, your conversion rate will suffer simply because sales teams need to work harder to get in contact with the lead. If an email is faulty or a phone number is fake, your sales team will waste valuable time hunting down alternative means of contacting the lead or worse, abandon it altogether.
  3. An Improved Customer Experience. There are few things more damaging to your brand than spammy, repetitive messages to the same lead. Prospective customers want to feel special and cared for by the companies with whom they choose to do business. That is definitely not the message that comes across when your data is bad and you are hitting the same email multiple times for the same campaign.

Data cleansing is in your best interest as a marketer, but it has a ripple effect, benefiting both your sales partners and the customer as well.

This is How to Go About Data Cleansing 

For marketers leveraging database management software, access to clean data can be a nearly automatic process. ReachForce offers a data cleanse solution that scrubs bad data and updates the information up to 4 times per year. That way, you have the peace-of-mind that your customer data is clean, free of duplicates, and with data validated.

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.