How are marketers improving data quality?
MarketingCharts staff
Through a special arrangement, presented here for discussion is a summary of a current article from MarketingCharts, a Watershed Publishing publication providing up-to-the-minute data and research to marketers.
While improving data quality is the biggest challenge to marketing data success, it’s also easily the most important objective of a marketing data strategy, according to new research from Ascend2.
Looking at the perceived effectiveness of tactics used to improve data quality, the study found two stood taller than the rest: validating contact data as collected (49 percent); and assigning data quality responsibility (47 percent).
The analysts note that the former has become a popular automated process that can be integrated into digital contact forms quite easily. Nevertheless, it also ranks as one of the tactics requiring the most effort (skill, time and expense) to perform, according to the survey’s 250 marketing influencer respondents. By contrast, assigning data quality responsibility appears to be one of the easier tasks.
Another popular tactic is integrating sales and marketing data, yet this is considered the most difficult tactic in terms of the effort required. In fact, research from Adobe and Econsultancy indicates that for almost two in three company marketers, the siloed nature of their organization prevents them from better using data in marketing.
A recent study from Allocadia linked poor data quality to restrictive reporting methods. Forty-two percent of respondents in an accompanying survey of more than 200 B2C and B2B professionals said that they are only able to run baseline reports on past marketing performance and another 13 percent admitted that they don’t know where all of their data lives so are unable to leverage it. That leaves fewer than half starting to or actively using advanced modeling, though that figure was higher among high-performing organizations.
Respondents are also using fairly basic tools, led by Excel and PowerPoint, for marketing performance management. Fewer than one in eight respondents are using purpose-built tools and have a roadmap for future technologies.
The marketing and finance relationship certainly has room for improvement, per the Allocadia survey results: respondents were twice as likely to say marketing’s relationship with finance is minimal to non-existent (28 percent) as they were to describe finance as a trusted strategic partner (14 percent).
- How Are Marketers Improving Data Quality? – MarketingCharts
- Marketing Data Quality Trends Survey Summary Report – Ascend2
- Marketing and Sales Measurement Data Cleanliness Remains A Challenge – MarketingCharts
- 2017 MPM Maturity Benchmarking Report – Allocadia
- What Are Organizations’ Top Data-Driven Marketing Goals and Challenges? – MarketingCharts
Discussion Questions
DISCUSSION QUESTIONS: How can marketers improve the quality of their data? What are the tried-and-true and promising new methods? Are siloed departments the biggest hurdle or do you see more pressing issues?
Poll
BrainTrust
Lyle Bunn (Ph.D. Hon)
Strategy Architect – Digital Place-based Media
Adrian Weidmann
Managing Director, StoreStream Metrics, LLC
It’s ironic that in the age of data in which we live, basic issues around data quality and access to data still haunt marketers. Given the amount of data marketers have access to and the sophisticated tools at their disposal, in many ways marketers have never had it better. The fact that some of the age-old challenges persist suggests that this is less an issue about tools or technology and more about analytical skills/training, internal process and, importantly, politics.
A key point is the 13 percent of honest respondents in this study who admitted that they don’t know where all of their data lives so they are unable to leverage it. This is huge! I can say that 80 percent of data is literally invisible to most of today’s systems that reside in retailer organizations. That “dark data” is critical to effectively managing your marketing processes. The first step is to augment your existing systems with cost-effective — and even some free — technologies that can uncover that dark data.
The age of digital marketing has evolved marketing from an art-form into a science. Digital marketing has been around for about a decade (in a serious way). Yet it is still the responsibility of the marketing leader to set the pace for experimentation and careful empirical observation, then decision-making. The tools are great and the numbers are available, but you have to know what to look for and to track the stats diligently.
As for quality of data, you need to triangulate. It is a matter of experimenting and finding out what is most reliable. For example, Twitter might state you have 64 conversions. Don’t trust it, prove it! Put in tagged URLs and triangulate with Google Analytics or your marketing automation tool’s metrics. Don’t trust the numbers, prove them!
Data reminds me of that question — which came first, the chicken or the egg? Data administration (capture, normalizing, etc.) comes before useful analysis, but because of the pressure, businesses want to apply data even with its shortcomings. Accuracy is data’s Achilles heel and always will be. The motivation of a consumer to assure the retailer has accurate data about them is very transaction-focused. Unless the aperture is widened in which retailers provide highly relevant and non-creepy suggestive selling, data accuracy will only matter to the retailer.
The top category for validating contact data as collected should take a jump this year as more retailers embed alerting by SKU on their websites. Today, user-supplied contact data needs validation because often users are either faking the data or making errors in supplying it. But when a contact email or SMS number is requested for alerts on the specific products the user is thinking about buying in the future, the data is nearly 100 percent accurate and does not need validating.
Although tangential, we (at Hubba) see this issue all the time in product content data. The burden of managing and syndicating this information has now shifted from the IT/supply chain teams to the marketing teams. They generally face two issues: They can’t find the information within their corporation (or the information is inconsistent) and even if they can find it, they need to get it out to multiple channels. They just can’t keep up. It is a patchwork of emails and spreadsheets. The end result is that brands, who spend billions creating great products and stories about them, don’t get to tell that story when the customer is making a purchase decision at retailers.
We try to help them with this by creating a single source of truth and allowing everyone to share it, both internally within a company and with all of their trading partners.
It is not an easy task but we are seeing a huge mindset change in the marketing world about data. Today it is mission-critical to be accurate. Otherwise you will lose out to your competitors.
The greatest problem facing marketers today is the proliferation of customer-facing data silos driven by multiple disconnected software tools and the lack of integration of past acquisitions. The traditional method of mapping all the data across all the applications usually does not succeed because the cost is too high in dollars and resources. The new approaches, which we are piloting for clients today, include using machine learning to better forecast needed data fields and assembling “data marts on the fly” to address the analytic needs of marketers and other executives.
The challenge is not to run after the next shiny new thing but to keep focused on the key drivers of ROI — what will actually impact the business in the short and long term.
Through to the end of the 1970s, marketers force fed the generations with must see advertisements printed coupons and newspaper adds. Today legislators are forcing the consumer to keep what’s left of the news papers viable with government publishing of materials nobody reads or ever did.
Social media allows for dismissal of advertisement by the viewer almost at will. As we are seeing the end of printed media and published learning text, the end of social media is also established and moving forward. Social media is feeling the effects we have here and are seeing advertising dollars dry up. The reason is simple: few of the investors are getting the results they need to own for continuance.
Those in search for reliable results are inundated with fingers pointing to issues that are true to the nature of information technology entrapment. We are being told that the needs are more hardware and software to keep in step with the evolving generations. The truth is, we need the generations to get in tune with how we are evolving. The beverages industry is doing this with the take-out industry, simply by claiming to have created what the public demanded. With that claim, they insist we come in and try it. This sort of innovation is called a promotion and can be done on any scale. The trick is to go where the consumers are and to measure success in every promotion sorting by the financial winners and losers. The 2 key elements for market research to monitor is participation and word of mouth proliferation and lifespan as in time to live.
Going long on IT solutions and input is as foolish as going long on buggy whips and beepers. Stay in front of the buyers learn what they want and sell it to them where and when they want it.
Data used in making marketing decisions has grown considerably. The age of Big Data has allowed marketers to go from collecting simple point-of-sales data, to more subtle information, allowing more complete and better informed marketing decisions to be made. Far more than simply gathering information on what items are selling where, to whom and at what time, today’s data-driven marketers are gathering more detailed information about how consumers and potential consumers respond emotionally to a product or brand, and what is most important to them in life beyond whether they prefer one box of soap flakes to another.
Improving data quality starts with the realization that some data points may seem intangible — but predictive intelligence can now accurately be used to better understand the emotional motivators that drive buying behavior.
Second, the data and intelligence being gathered must be actionable, and used to enhance advertising and other digital content, coordinate the omni-channel experience, and design new products to connect with the customers’ emotions.