Perfect Data: A Marketer's Dream

Imagine that you have a marketing database populated exclusively with accurate records, each one complete. No blank or null values. No missing fields. No bad contacts, invalid email addresses or disconnected phone numbers.

your marketing to reach: contacts with the right personas and demographics (plus firmographics for B2B). Your data is so good that you can easily pick these people out from the crowd. You have all of your customers’ purchasing histories and details about each time a they have engaged with your brand—web visits, social media shout-outs, infographic downloads.

Your ideal database also gives you access to performance data for every marketing campaign you’ve ever deployed. Any scrap of information you can imagine that might be relevant to your marketing efforts? It’s all there.

Even more fantastic, imagine the impact of all of this good data:

• No more bounced emails
• No time wasted calling bad phone numbers
• Better insight into campaign performance, allowing you to optimise marketing programmes
• The ability to create more sophisticated consumer and business lead scoring
• The opportunity for advanced analytics to inform strategic decisions.

You wake up from this wonderful daydream only to realise that perfect data is a fantasy and that “bad data” is preventing you from dreaming about what’s really important—like surfing in Maui or hiking the Great Wall.

The good news is that you don’t have to (and shouldn’t try to) fix all of your data—this would be expensive and impractical. Small changes can yield big results. In fact, for every 1% in data quality improvement, marketing can generate 5-6% of incremental revenue.2 By building specific and actionable data quality goals and aligning them with marketing objectives, you can deliver a significant impact on the business.

Why You Should Care About Good Data

From overall strategy to specific marketing efforts, accurate data positions you to be as efficient and as effective as possible—which ultimately allows you to deliver a more impressive return on investment. The following are some specific ways in which you can use good quality data to improve the bottom line.

More accurate and more useful reporting

Good data enables better and more useful reporting. If you want to ensure that you’re accurately measuring customer lifetime value, retention or attrition, correctly gauging campaign performance, and truly identifying your best customers, you need to be confident that you’re reporting on an accepted version of the truth. This requires data that is as accurate and complete as possible. When your data has errors or holes, so will your reports and the customer insights and marketing strategies based on the data.

Good data enables good decisions

Remember those accurate, useful reports we just talked about? They will help you to uncover valuable insights that would likely be invisible in a “dirty” database. For example, a clean database allows you to identify which variables distinguish your best customers from all others and which variables are irrelevant.

Having a clean database also paves the way for more advanced analytics, which can provide further understanding of your customer. Here are just a few of the types of insight strong analytics can reveal:

• propensity to purchase
• your best upsell and cross-sell opportunities
• retention and churn
• market white space
• best locations for new brick and mortar stores
• ideal channel partner profile

This information can help you make better decisions around prospect selection, segmentation, messaging, content strategies and list purchases—all helping to maximise marketing return on investment.

Better campaign performance and efficiency

When you improve both the depth and breadth of your data, marketing programme performance and marketing efficiency improve, too.

Because better data allows you to better segment and target your prospects and customers, you will be able to provide each consumer or business contact with more relevant communications. This drives more responses from higher quality leads, ultimately resulting in more purchases.

The inherent value of data

While many marketing leaders value the outcomes that strong data can provide, they overlook the value of data itself. One survey found that more than 50% of CMOs listed optimising the sales-marketing funnel and gaining greater audience insight as top strategic priorities. Only 19% cited improving data hygiene as a strategic priority—but without good data, it’s virtually impossible to realise either of those other objectives.

An Aberdeen Group study found that “the business benefits of data management are often greater than the sum of its individual performance boosts.” Those benefits include annual improvements in organic revenue, customer retention, and customer satisfaction, as well as decreases in operational costs.

Additionally, maintaining good data ensures that your marketing and lead generation spend doesn’t go to waste. Let’s say a record for a contact who has responded to campaign costs about $50 to obtain. That means it would cost approximately $250 million to replace a database of 5 million records. If 25% of your data decays annually (which is industry average), you’ve lost 1.25 million contacts worth $62.5 million. If you can salvage 20% of those contacts, you have saved $12.5 million would have otherwise gone to waste. This doesn’t even include the potential value of these prospects to your business in sales revenue. Although we can argue that the value of a contact may be lower or higher, the bottom line is that a reasonable remediation effort can—and will—lead to significant business results.

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What Good Data Looks Like

We have determined that good data is important—so how do you evaluate your database health? Start by asking yourself what you need to get from your data. Is it fit to serve its intended purpose? Marketing data has a particular set of requirements; other areas of the business will have different needs.

Examine the criteria below to rate your marketing data. The higher your numbers, the healthier your data. The lower your numbers, the more room you have for improvement.

[Download PDF to see Table]

How to Enrich Your Data]

Thankfully, you don’t have to turn your world upside down to improve the quality of your data. Using a 5-phase approach, you can identify data issues, develop a remediation strategy, and source and integrate the right data for the right purpose to achieve maximum results.

[Download PDF to see Diagram]

Phase 1: Perform a Data Audit

The data audit forms the baseline for your data improvement strategy by giving you a clear picture of what data you have and how well it aligns with business imperatives and marketing initiatives.

Develop a focused audit strategy that starts by breaking the data into specific segments that make sense for your marketing or business objectives. This can be by products, solutions or services, by region, by business unit, by brand, etc.

The next step is to prioritise your segments for the audit. Prioritisation allows you not only to more effectively manage the audit work, but also helps in setting next steps for data remediation activities.

Once the audit priorities are established, be sure to make it an ongoing process to create a steady funnel for your data improvement pipeline.

Phase 2: Analyse Existing Data

Now that you know what data you have to work with, it’s time to analyse the data to see where it can be improved. Harte Hanks uses its Four Box Methodology as a consistent data assessment framework that you can use as the basis for your data improvement strategy.

The basic idea is to take your audited data and create your own criteria for placing it into one of four quadrants as follows:

[Download PDF to see Table]

As you can see, the goal is to move as many contacts as possible into the upper right-hand box. The boxes that the customers fall into inform what plan of action to take with each of the data sets. The audit analysis also provides a baseline by which data quality and remediation activities can be measured over time.

Of course, this is a very high-level example. To get the best results, take some time to carefully create your own criteria and action plans based on your unique situation and data set.

Phase 3: Build an Ideal Profile

Once the audit has been completed, the next step is to reevaluate contact and account profiles to determine if are there other data points that would allow you to create better segmentations for marketing and sales. Sources for web and social data are becoming more available and easier to access, allowing you to build out your profiles. However, the purpose of building an ideal customer profile is to help you to focus your efforts on making data fit for its intended purpose as opposed to creating a “perfect” record for every single contact or account. Strive for a balance between what’s needed to improve marketing and sales effectiveness and the costs of purchasing, housing and maintaining additional data sources. Given data quality, availability and restrictions, the “ideal profile” will vary to some extent by country or region.

A common set of desirable data includes:

• Core Contact and Account Attributes: standard contact profile (name, email, address, title, company) and account firmographics (company revenue, industry, location, number of employees) plus relevant account level transactional data
• Extended Attributes: supplemental or derived data, such as installed base, wallet size, role, cross-channel shopping, propensity or other modeled scores
• Social Attributes: includes data on sentiment, interest and intent derived from social interactions or social networks; can be at the contact or account level
• Behavioural Attributes: engagement activities that may include sites visited, content consumed, campaign response, events attended, etc

Keep in mind that the availability, quality and accessibility of data will be different for each segment, as well as which elements are desired, so look at them separately and adjust your criteria for each as necessary.

Phase 4: Choose Your Tactics—Update, Append, Enrich

Once you identify the components of your data that need the most attention, there are a number of remediation tactics you use to improve data quality.

[Download PDF to see Diagram]

Ultimately, you will want to capture both explicit (facts) and implicit (behavioral and derived) data to develop a more complete contact or account profile. Having good data for both can dramatically improve insight and the ability to segment more effectively—which means connecting with the right people with the right information at the right time. However, we see behavioural data as being immediate and actionable, allowing organisations to act quickly to convert customers who are ready to purchase.

Phase 5: Deploy and Repeat

It’s not enough to complete this exercise once and move on. Customers – and their data – are a constantly moving target. A dedicated and continuous process of data auditing, analysis and improvement is required to maintain the benefits of your efforts. If you don’t have the time, staff or resources to keep up with your data improvement strategy, it makes sense to outsource this task to an expert partner in data quality.

Conclusion

You now know that perfect data unfortunately does not exist—you will never completely eliminate all email bounce backs or disconnected numbers. However, pretty good data does exist, and it can dramatically improve your bottom line. Aside from its inherent value, good data allows for more accurate and useful reporting, enables good decision making, and improves marketing efficiency and effectiveness. Even small improvements in data quality can generate big results.

To see these big results, it’s just a matter of auditing and analysing your current data, developing an ideal customer profile and remediating with the most appropriate tactics.

Now that you have a framework in-hand to create your data remediation strategy, it’s time to get to work. Your customers – and your competition – won’t wait. Besides, don’t you want to get back to dreaming about surfing in Maui?