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Personalization Failure: Your Data Cleanliness Could be Hurting your Credibility

Reading Time: 6 minutes

Despite the rise of social media platforms, AI chatbots, and texting, email is still one of the most powerful tools in your sales arsenal. Not only do email campaigns create $44 for every $1 spent, but nearly 60 percent of marketers admit email is their single biggest driver of ROI. Unfortunately, every other salesperson is also aware of the value of emails. As a result, the average person receives around 120 sales emails everyday. So, how do you make your emails stand out in this deep ocean of never-ending spam, business communication, and sales pitches?

You use personalization. Seventy-four percent of businesses admit personalized campaigns get more traction, personalized subject lines have 50 percent higher open rates, and personalized CTAs convert 202 percent better than promo lines. In other words, personalized emails increase open rates, boost conversions, and increase interest. And these benefits aren’t limited to emails. Personalized social media campaigns see greater reach and higher engagement scores, personalized text messages and phone calls improve close rates, and personalized ads generate higher sales.

Here’s the problem: Gartner predicts that 80 percent of marketers will abandon their personalization efforts over the next few years due to a lack of ROI. A significant number of salespeople struggle to personalize due to a fundamental lack of understanding. Savvy strategies and incredible AI may be the brains of the operations, but data lies at the center of every personalization campaign. And a few dirty pieces of data can sink your entire personalization budget.

Data & Personalization Are Intrinsically Connected

At its heart, personalization is the ability to leverage data to deliver the right messages to the right prospects at the right time. For example, your marketing and sales team may use past whitepaper downloads to target users with ads that assume they know a little about your product. Or, your sales team may use CRM data to deliver emails with customer names and information to build trust.

In other words, data helps you build relationships, create trust, and generate tangible interest in your products or services. In fact, 64 percent of customers expect every interaction to be personalized. So, having rich, clean data is a critical driver of sales success. Your customers want to continue a journey — not restart at every touchpoint. If they’ve contacted sales before, they want you to resume that past conversation. If they’ve consumed content, they expect you to know that they’re somewhat knowledgeable about your product/service. Above all, customers expect you to know their name, interest level, and prior brand experiences.

When Personalization Goes Wrong: The Importance of Data Quality

It only takes one incorrect data point to sink a personalization campaign. Imagine calling a customer by the wrong name, sending an email to the wrong address, or targeting the wrong audience with your semi-expensive ad campaigns. All of those situations happen daily. And they happen to nearly every business. In fact, Gartner estimates that poor data costs the average enterprise $9.7 million per year. Across the entire United States, IBM suggests poor data is costing companies over $3 trillion in lost opportunities.

Your prospect data will decide the future of your personalization campaigns. Every piece of incorrect, inaccurate, and irrelevant data presents a tangible threat to your company. Unfortunately, that threat is difficult to measure. Instead of robbing you of liquidity on the front-end, poor data quality minimizes opportunities and growth. So, if you’re struggling to grow, grasping at straws to convert, or watching your competitors outpace you in the market, there’s a chance data quality is to blame. Over time, small inaccuracies create webs of sales lies. Instead of taking bold steps towards hyper-personalization and customer-centricity, your organization is left confused, stagnant, and desperately searching for answers.

For example, salespeople may be working with outdated prospecting lists. So, instead of targeting rich, intent-driven leads, your salespeople waste time on low-quality leads. Or, alternatively, your prospecting lists may be drenched in inaccurate data. So, instead of creating a lukewarm cold call, salespeople call prospects by the wrong name, assume they’re at the wrong stage in the funnel, and provide them the wrong pitches.

Luckily, you can fix poor data quality (thus fixing your personalization woes). But it requires some effort, strategy, and time.

Data Cleanliness Best Practices

Since data is so crucial to your personalization efforts, it pays dividends to keep it clean, consistent, and accurate throughout the data lifecycle. At Sapper, we prefer bottom-up data cleanliness strategies. Once you tackle the input, it’s easy to make sweeping changes to existing data. Here’s are the three most important best practices for data cleanliness.

1. Create a Game Plan

Before you start fixing data, you need to understand your data expectations. You should be able to answer all the following questions before you commit to a data cleanliness overhaul.

  • What are your data expectations?
  • How will you measure data cleanliness?
  • What types of returns do you expect?
  • How will you train employees on data cleanliness?
  • What types of organizational changes do you need to make to facilitate clean data?

In other words, this is the organizational side of your transformation. Discuss your goals with stakeholders, develop the right KPIs, and measure the effectiveness of your cleanliness campaign. We can’t give too many actionable insights on this section — since every organization has a very unique data ecosystem. But it’s important to sit down and discuss the finer details before executing any data strategies.

2. Fix the Source of the Problem

Dirty data doesn’t spawn by itself. It’s almost always the result of poor inputs. For example, you may lack standardization. Two salespeople may enter a phone number into a Salesforce field in completely different ways. If one salesperson enters (555) 555 – 5555 and another one enters 555-555-5555, you have a problem. Not only does this small difference impact metadata and searchability, but it can create havoc for your BI tools and back-end analytic platforms.

Training plays a massive role in solving this problem. Every employee should fully understand how to enter data correctly and what mistakes to avoid during the data collection process. To do this, you’ll need robust data policies and savvy data collection methodologies. If employees have guidelines to help them understand how to enter data correctly, you can avoid most of these mistakes. We’re not aiming for perfection. But you should be able to get pretty close.

3. Work Backwards

Once you’ve solved the input, you can start tackling the output. Chances are, you have troves of “dirty” data in your IT ecosystem. Now, you need to find it and fix it. You can use email verification tools, automated data health tools, or manual throughput to start digging through your mountain of data. While fixing existing data mistakes can save you a ton of money in the near term, solving the original input can massively impact your marketing campaigns. So, don’t skip the previous step. Fix the input, then use the best available tools to start cleaning your existing data.

How Sapper Can Help

At Sapper, we help companies grow their businesses with robust, clean, and highly actionable prospect lists using world-class data cleanliness practices. To learn more about how Sapper can fill your pipeline with clean leads, contact us. Having issues generating leads in the first place? Check out our guidebook on game-changing lead sequences.

How to Create A World Class Conversion Engine

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About The Author

About Sapper Consulting

Sapper's sales prospecting team becomes a natural extension of your existing sales efforts, helping you find new leads that are a great fit for your business.

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