Prep for 2020 Marketing With Clean, Personalized, Predictive Data

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As 2019 closes, AccuList’s data services clients have a year’s worth of multichannel customer, campaign and sales information to analyze and inform 2020 plans. So what are the big trends that the data pros foresee will deliver maximum ROI?

Data Hygiene Issues Remain a Priority

Clean, up-to-date, quality data is still the basis for good marketing analyses and campaign planning. A November Business2Community post by marketer Dan Moyle helpfully summarized the key data cleansing tasks that businesses need to undertake to hit the ground running in 2020. After all, it’s estimated that 20% of the average contact database is dirty, so this is not a trivial effort. Increasing marketing efficiency, response and customer loyalty, requires removing data errors and inconsistencies. Start by monitoring data for issues such as duplicates, missing information or bad records to figure out how and where they are occurring. Then standardize processes at each entry point. Next validate the accuracy of data across the database by investing in data tools or expert data services, and commit to regular cleansing and maintenance of data quality. Identify and scrub duplicates. Once the data has been standardized, validated and de-duped, improve its analytic value by using third-party data appending sources (to flesh out demographics, psychographics, firm-ographics, purchase history, etc.) for a more complete customer picture. Establish a feedback process to spot and update, or purge, incorrect information, such as invalid e-mail addresses identified by a campaign. And communicate standards and processes to the whole team so that they understand the value of clean data in segmentation targeting, lead response, customer service and more.

Using Data for an Agile, Personalized, Customer-Centric Edge

Data trends figured prominently in the 2019 Martech Conference and a recent article from martech firm Lineate highlights a few keynotes, such as the role of data in personalization. When a 2019 RedPoint Global survey of U.S. and Canadian consumers finds that 63% expect personalization as a standard of service and want to be individually recognized in special offers, personalized marketing is clearly a competitive essential. Expect to see use of Artificial Intelligence (AI) and machine learning (ML) increase in 2020 as personalization tools. Machine learning is when a computer is able to find patterns within large amounts of data in order to improve or optimize for a specific task. For example, for more personalized offers and messaging in acquisition, this means using ML to recognize if people from certain areas are more likely to respond to a specific offer or which past high-response special offers may resonate in future . Personalization is also key to the customer-centric experience proven to drive long-term retention and brand loyalty–as opposed to getting the same message again and again. When personalization is combined with elimination of data silos and creation of a single customer view across channels, marketing becomes especially powerful. Indeed, integrated database development and the elimination of data silos are also key to the growing “agile marketing” trend. Agile marketing breaks down team silos (which assumes breaking down data silos) in favor of teams focusing on high-value projects collectively. According to a 2018 survey by Kapost, 37% of businesses have already adopted agile marketing, and another 50% said they haven’t yet become agile but expect to be soon.  

Taking Data Insights From Retroactive to Predictive

Looking ahead to 2020, marketers should also consider adding predictive modeling to their toolkit if they haven’t already done so. Why? A study by ClickZ and analytics platform provider Keen found that 58% of marketers using predictive modeling experienced a 10%-25% ROI lift, while another 19% saw more than a 50% uplift. While retroactive campaign data can be very useful for reporting and results analysis, it’s not always as good for informing future multichannel directions, for optimizing media investments, or for quick execution and performance assessment. In fact, nearly 80% of Keen/ClickZ survey respondents felt they’d missed opportunities because of slow or inaccurate decision-making using non-predictive data reporting. For example, standard data analysis often fails to span all channels (e.g., online video vs. store-level programming) and mistakenly gives most credit to last-click channels such as search or transactional activities. In contrast, the Keen/ClickZ survey found marketers using predictive modeling boosted results in multiple areas, including a better understanding of the target audience (71%), optimizing of touchpoints on the customer journey (53%), and improving creative performance (44%). Predictive modeling also can help businesses synthesize large volumes of data, a key concern for many; in fact, 38% indicated their current measurement solutions do not support the scale of their data.

 

Make Clean Data a Top Priority for Effective B2B Marketing

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As business-to-business marketers craft their fiscal 2020 budgets, it’s important that complex issues such as analytics, automation or AI do not distract from a core investment for achieving ROI: clean data. Certainly, AccuList stresses to all its list hygiene and management clients, whether for house lists or rental prospecting lists, the importance of data quality for targeting and response, and a recent blog post by b2b data management firm Synthio confirms the basic steps for data hygiene.

Start With a Clear Data Plan

When 94% of B2B companies suspect inaccuracy in their databases, any marketers who do not prioritize data hygiene have their heads in the marketing sands.  That starts with a data plan. A good data plan will decide on the data-quality key performance indicators (KPIs) needed to achieve business goals. The plan will survey existing contact and account data and determine how to measure health in terms of data accuracy and completeness and how to maintain data hygiene tracking on an ongoing basis. It will look to see if there are important parameters for KPI success that the existing data does not address.

Standardize, Validate and De-Dupe Contact Data

What are the basics of data health and hygiene? Before cleaning data even begins, marketers need to check that important contact data at the point of entry or download is standardized. This will make it easier to catch errors and duplicates and to merge data from multiple sources. There should be a standard operating procedure (SOP) that defines fields, formats, and entry or upload processes to ensure that only quality, standardized data is used. The next step is to validate the accuracy of the data. Although a manual process might work for a small database, and there are tools and imported lists for cleaning data, advanced data hygiene is probably best handled by experts like AccuList, which can match contact addresses against USPS verification standards and change of address databases as well as update e-mail address changes. With standardized, validated information, data sets can be seamlessly merged and purged of duplicates. Why worry about duplicates? Duplicate records hobble CRM efforts, waste dollars in marketing campaigns, undermine the Single Customer View essential for targeting and response tracking, damage customer relations and brand reputation, and result in inaccurate reporting that can mislead marketing strategy.

Append Missing Data Parameters

Most b2b house databases have data for each record, such as contact first and last name, e-mail, company name and business address. But complete data for all records may be spotty, and some desired data may be missing altogether, such as title, phone number, company annual revenue, tech stack, purchase history, etc. Wouldn’t it be great for targeting and response to fill in the blanks? Data appending can enhance a house file with hundreds of variables from outside lists, including business “firm-ographics” on revenue, industry, employee numbers, etc.; opt-in e-mail, and telephone numbers. Self-reported LinkedIn data is another source that can be used. For more detailed data cleaning tips, see Synthio’s full article.

Make Sure You Have a 2019 Data Hygiene Plan

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As marketers prepare to launch their 2019 campaigns, they should make sure that a complementary data hygiene plan is in place, and certainly AccuList USA data services stand ready to aid in ensuring the quality, up-to-date, enriched data essential for achieving marketing results.

Why Does Clean Data Matter?

Marketers don’t want to join the 88% of U.S. companies whose bottom lines are hurt by dirty data, based on Experian research. The top areas impacted by poor data practices are marketing (66% of companies) and lead generation (80% of companies), according to DemandGen. Dirty data leads to poor targeting and ROI for marketers, reduced revenue from customer acquisition and retention, wasted company resources and misdirected strategy. To avoid that fate, marketers need a plan to regularly fix any customer and prospect data that is incorrect, inaccurate, incomplete, incorrectly formatted, duplicated, or irrelevant, plus to enrich the database via appending of relevant but missing customer parameters.

Developing a Data Cleansing Strategy

Pete Thompson, founder of DataIsBeauty.com, has put together a useful primer for developing a data hygiene plan. Start with the basics: Decide what data is important for business decisions and estimate the ROI of data quality improvement. Then review existing data processes: types of data captured, where it comes from and how is it captured, the standards for data quality, how errors and issues are detected and resolved, etc. Other questions include the main sources of errors, methods for validating and standardizing data, methods for appending or combining multiple sources, automation used if any, accountability for data quality, and measurement of data ROI.

Key Elements of a Data Hygiene Plan

Without going into detail, the basic steps of the data plan will start with creating uniform data standards, preferably applied at the point of data capture. Then develop a data validation process, applied either when data is captured or, if that is not possible, at regular intervals for data already entered. After data has been standardized and validated, you can append missing fields by cross referencing with multiple data sources. Streamline the process through automation tools and scripts, saving time and money and reducing human errors. However, while it may be tempting to start with automation, Thompson cautions against putting the cart before the horse; success requires having data standards and a proven validation process in place before automating. And then set up a monitoring system of the hygiene process, whether automated or not, via random test samples and back testing, and implement periodic checks.

For regular monitoring, or overall scrubbing without an automated regimen, experts suggest a quarterly hygiene review for databases of 100,000 records or more, and semi-annual cleaning for smaller databases. Based on our own years in the data business, we think the best advice from Thompson and other experts is to enlist the services of data processing pros when hygiene is due!

Check out more details from Thompson’s data hygiene plan.




Why You Should De-dupe Your Data

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In today’s data-driven marketing, data is not only the most important asset that your company can have but can also make or break your campaign. Having clean data impacts not only marketing activities but also impacts your reputation, operations and decision-making. De-duping is one of the most important aspects of overall data hygiene. Duplicates can be found on many levels of data; they arise at the household level, individual e-mail level or company level. But before you can de-dupe your data, you must make sure you have a clear definition of what a duplicate is. Some businesses de-dupe based on a household address for direct mail campaigns, others on an e-mail basis for e-mail marketing campaigns, and some de-dupe based on the company level. If you are still not convinced that you need to de-dupe, consider the following benefits:

Avoiding Different Offers to the Same Customer

Having direct mail going out to the same household can be costly, and it can also be extremely embarrassing. For example, you send two different direct mail creatives to the same household. As one of the records was a customer, you decided to provide a returning customer 15% off, while the other record was marked as a prospect and only got 10% off. Now the person opening both direct mails will be confused by having two different discounts, and the company also can face a PR nightmare.

Cutting Unnecessary Cost

It goes without saying that having duplicates increases your cost. For example, assume you are doing a direct mail creative which costs you $5 per mailing. Your list contains 10,000 recipients. The total cost of mailings therefore is $50,000. If you decided to de-dupe, you would find out that 10% of your mailing list was duplicated. Therefore, $5,000 was a waste of resources. It would have been much cheaper to de-dupe prior to deploying your campaign.

Good Analytics for Decision-making 

Analytics is important not just from a perspective of understanding how your marketing and sales is performing but also from a decision-making perspective. By having duplicates in your CRM, you are going to be double-counting your list capabilities, miscalculating your true growth rates, and getting the wrong rate of responses. If you are looking to make a decision on future campaigns, basing it on duplicate data will give you the wrong list count, wrong budget and possibly the wrong creative picked (especially if you are basing it on an A/B testing done previously).

Reducing Customer Service Confusions

If there are duplicates in your CRM system, having clients call in, e-mail or come into the store will make it difficult for staff to track down the right individual. For example, Mary Smith is found twice in your CRM with the same phone number. She calls in to your customer support to inquire about her order status. Your customer service rep decides to pull up the customer account by phone number and finds two records. Now she has to put the customer on hold while she checks both accounts to try to locate the last purchase before she can even assist the customer. Not only is it wasting everyone’s time and making customer service inefficient, it also makes the customer have a bad customer service experience.

Preventing Potential Loss of Sales

Finally, the biggest impact that duplicates have on your business is a potential loss of sale. If you have duplicates, you do not have a true view of all prospect or customer activities. Therefore, you could be excluding prospects from a sales call because your lead scoring system indicated that they are not ready. However, if the data from both records was combined, you would have all signals indicating they are ready to be passed on to sales. With duplicates, by the time you figure it out, a customer may have already lost interest and gone with your competitor.

You can easily de-dupe your list by using a de-duping tool that will require less effort to identify duplicates and establish a master record than is required to deal with the consequences of duplicate data. De-duping should be part of your data-cleaning initiative, either prior to any major campaign or on a yearly basis.

If you are interested in data clean-up and use of a de-duping tool, contact guest author Anna Kayfitz, CEO of StrategicDB Corp.