The State and Drivers of Data Marketing

What matters most is the optimization of the customer experience, relevance and (perceived) customer value as a driver of business value. Data-driven marketing certainly is not (just) about advertising and programmatic ad buying as some believe. Nor is it just about campaigns. On the contrary: if done well, data-driven marketing is part of digital marketing transformations whereby connecting around the customer across the customer life cycle is key.

Very succinct vision of what data-driven marketing is, it's all about the customer experience.  The advent of "big data" was nothing more than gathering extra data about the customers.  Gathering data is only the first step of the process, albeit a time-consuming one.  The good news is after the hard work of gathering the data has been completed, the harder part starts.  Once you have data, making sense of the data and creating actionable outcomes to enhance the customer experience becomes the goal.  This is very hard work.  It takes plenty of analysis and insight to reach this goal.  But the companies who will do this the best will be the ones that succeed in the digital age.

Among the key takeaways of the data-driven marketing report by the GlobalDMA:

• 77% of marketers are confident in the data-driven approach and 74% expect to increase data marketing budgets this year.

• Data efforts by far focus on offers, messages and content (marketing) first (69% of respondents). Second ranks a data-driven strategy or data-driven product development. Customer experience optimization unfortunately only ranks third with 49% of respondents.

• Among the key drivers of increased data marketing: first of all a need to be more customer-centric (reported by 53% of respondents). Maximizing efficiency and return ranks second followed by gaining more knowledge of customers and prospects.

I believe the first step in the process is understanding where the puck is going to be and skate in that direction.  Marketers are understanding this data revolution is coming and they are saying the right things in surveys.  The real question will be how to get there.  It's easy to identify problems, it's hard to implement solutions.  The marketers who will show they are adept at change will thrive in this new paradigm.  

Customer analytics is something I have focused my entire career.  In the casino industry we have had the optimal opt-in mechanism for many years and have collected amazing amounts of data about our customers behavior.  We have used this to create targeted marketing campaigns to our customers, so I believe in the direction the entire industry is taking.  Always start with the customer.  It will lead to creating better experiences and more profitable results.

 

Data is the First Step to Marketing Automation

I have implemented many marketing automation solutions over the past decade and one of the perplexing findings is how organizations put the cart before the horse when they are installing their solutions.  I like to say marketing automation solutions are "dumb".  Not the kind of dumb as in "this is stupid, why are we implementing these solutions, why not do something else".  They are "dumb" in the essence of they need help from something else to be successful.  They cannot work on their own.

Marketing automation tools are a slave to the underlying data.  All marketing automation tools do is query data and create metadata that is used to create content and messaging for your customers.  Now I am minimizing the importance of the marketing automation tools in that sentence, but from a high level, it works.  

Since the underlying data is what drives the marketing automation tool, that data is the first step in implementing the tool.  Without the proper data, your implantation will fail.  Getting the data into the proper format for consumption from the automation tool is the most important step of marketing automation.  

Understand the problems to be solved

Write out all the different types of campaigns or communications to be run with the automation tool.  This step is vital to understand if there is a gap in your data collection strategy.  Also, this identifies if the data is structured properly to even run these types of campaigns.  This step comes before buying a marketing automation tool.

For example, I want to send a reminder email to all customers who bought a television that specific cables will enhance the performance of their new purchase by 50%.  For this, the data will have to be structured to understand which customer bought a television set, along with cables because you don't want to sound like you don't know your customers, within X amount of time, their email, mailing or app device ID, and the channel they prefer to be communicated with.  Now the data team can make sure they have the proper structure for just this one use case. If the data can't be structured accordingly, then the marketing automation tool will not be able to deliver this campaign.

Define success for the campaigns

This can be a simple sentence in each case.  What this determines is how the analysis of the campaigns performance will be achieved.  Analysis is also part of the marketing automation tool implantation, because I guarantee you that the executives will want to know the impact of this large investment, so the data needs to be prepared to answer these questions.

For example, I want to see the redemption rate and revenue generated, along with the expenses for delivering and cost of goods for the customers who returned to the store and purchased upgraded cables for their televisions.  For this the data will have to meld together the ID for the offer, in this case the cable, along with the purchase item along with the expense data from the marketing automation tool and the sales system.  These tasks aren't easy, but they will pay dividends if this legwork is done upfront.  There is nothing worse than flying blind with your marketing automation..  

 The expectations for campaign execution times

This is one that almost always gets missed.  I have heard of campaigns that run almost all day because the data is not organized in a fashion that is not optimized for the marketers.  That kind of performance may be acceptable if the campaigns are run once a month, but for most businesses that is not the speed of digital marketing.  

For example, I want to be able to run the campaign for the television purchasers every day.  This includes time to run the automation, send out proofs for the collateral and have the deliveries out to the customer by 10AM.  This allows the data team to be able to optimize the data structures to make sure the data can be pulled fast and efficiently for all your automation campaigns.  

This by no means is an exhaustive list, but it is a start to having a successful marketing automation implementation.  No matter how many bells and whistles the marketing automation tools have, if the data does not support the wants and needs of the marketer, it doesn't matter because the tool is "dumb".  It needs the data to perform magic.     

Turn Your Data Into Smart Data

Great insights from Scott Houchin regarding data.

To harness and convert data into stronger business strategies and overall profitability, approach data practices with a holistic integration of people, process and technology, following three key steps: collection, strategy and alignment.

A data strategy is the first step in becoming a data-driven organization.  Setting up the structure and expertise of the organization has to start before jumping into data strategies.  This can happen outside of the confines of IT.  The business leaders should own the data, as long as they have the expertise and knowledge to do so.  Try to set up procedures to be agile with your processes.  The longer it takes to implement changes in data, the less of a competitive advantage your organization has.  It will also be near impossible to become data-driven if there is a constant wait for data to be delivered to the end users.

Collection

Start with a clear understanding of project goals and requirements to guide the collection process. Establishing this helps ensure data collected is “smart” or meaningful. Collection shouldn’t narrowly focus on new data. Many organizations already have a goldmine of owned data that should be tapped. To make the most of historical data, scan legacy systems, such as social pages or purchase history, map findings back to strict uniform terminology, and fill in the gaps where data is missing across the organization.

Having a process for collecting new data and examining historical data up front ensures quick and accurate collection, minimizing time spent on governance practices and carving down unnecessary data sets.

There is a treasure trove of data already being collected in most organizations.  Ensure that this data is being properly collected and stored.  The goal is to ensure as many people can get to the data as possible, data democratization.  If data is stored and is hard to get to, takes complicated joins and there are no tools available to the organization to easily access the data, then more has to be done to reach these goals.

Strategy

Once data is collected, work with data-marketing specialists to analyze and align functional uses and marketing’s business goals. This requires a team of analysts and strategists who have both high levels of industry and domain expertise to identify sources, manage collection and road-map operations processes.

Teams of analysts can help organizations identify, collect and integrate data from sources and channels, like web traffic, Facebook, Salesforce, etc., into a proprietary database. Once established on a datamart, it can be integrated into current campaign tools through human labor. Having this data integrated into marketing tools gives brand-side marketers the insights to improve customer experiences, measure performance of digital assets, predict customer decision stages, etc.

Data should not be financial focused, it should be customer focused for the greatest impact on ROI.  Marketers have to own their data.  Hiring analysts and data domain expertise is imperative for success.  If ownership lies outside of the marketing resources, there is a much higher likelihood of failure.  Remember, CMO's and CIO's don't speak the same language.  

Alignment

Another example can be demonstrated with IT and marketing. Marketers spend more on technology than some IT departments now, but need alignment to ensure data is stored, platforms are integrated and in-house technical support is available. Alignment between these two departments appeases both marketer’s need for autonomy and IT’s domain over platforms, allowing for the integration of datamarts into other units’ datasets from the onset.

IT is still very critical for success with this strategy.  Just because IT does not own the data, doesn't mean they aren't extremely important.  IT needs to ensure the network is working, data is flowing and collection tools are working.  They also need to be support for when things break and they should control the access to the systems.  Make sure IT understands the goals and agree on the toolsets being chosen, so they can support them.  

Are CMOs wasting money on faulty marketing analytics?

Manji Matharu writes:

CMOs are now at a crossroads between data quality and data results. It’s no longer enough to dabble in analytics and come out with the richness required for informed decision-making. The business needs integrated systems across IT infrastructure, and marketers — not IT pros — must champion the call for improved data controls and governance as their cause.

Data quality is the first step in all marketing processes.  Ensuring this is boring and hard, but it is a necessity.  This is the first step when I come into an organization, determine the quality of the data and work to fix that.  Once there is a trusted version of the truth, marketing analytics come to life.  

The Case for Why Marketing Should Have Its Own Engineers

Today, he runs the marketing team like an independent agency within the organization complete with its own engineers — a strategy he highly recommends for small teams that need to get a lot done fast.

An interesting article to set up an in-house agency to support all of marketing.  As a database marketer, I truly believe the team needs its own database and its own engineers to maintain this database.  It has to be separate from the IT processes that slow down progress.

Why?

Why shouldn't marketing data be included in the rest of the organizations data?

The simple answer is time.  Most data put into data warehouses are used for analytics.  Sounds just as important right?  Analytics is the driver of making money in the organization correct? 

Sort of.  This data can also include financial data that has different processes based on financial rules, especially for public companies.  Some data might include credit card information, which need to be PII compliant.  This data needs strict data governance and encryption of sensitive data.  All of this takes time.

Time is the enemy of marketing.  The amount of time it takes to get data into a marketing database relates to an amount of revenue that is being lost.  Most data requested into a marketing database is used right away in segmentation for campaigns.  These campaign changes either drive revenue or save on expense.  Having engineers able to get data into the marketing database in an expedited process gives an organization a competitive advantage. The quicker new data equals the more efficient database marketers.  All this leads to more money to the bottomline.  

Interactive Data Visualizations - It's Still About the Data

With so many data visualization tools out in the marketplace, it is a wonder why most organizations are still struggling to get these easy to build dashboards adopted throughout the organization.

It usually comes down to the data.  The data still has to be accurate and up to date and reliable.  So many organizations still struggle with this.  I believe it comes down to structure within the organization.  How does IT still control the data in many organizations?  IT tends to create processes and documentation that takes data forever to get into the hands of the users.  

It all depends on the size of the organization.  When organizations are very large, this type of process and documentation is needed.  Most organizations are not this size.  The data is used by a handful of people within the organization and a more agile approach to data needs to be taken to always have the best data at the soonest possible time.  Long lead times and processes that make moving things forward difficult lead groups within the organization to get their own data in many different ways and this leads to many different versions of the truth and lack of trust in the interactive dashboards.

Organizations need to move the data ownership into the hands of the data users to ensure one version of the truth.

The Chief Data Officer: An executive whose time has come

I often ask people whether they know what Netflix, Harrah’s, Amazon and Wal-Mart have in common? The answer is pretty simple. They use data analytics to leave their competitors in the dust. Many other businesses are trying to do the same, spending millions of dollars on data software.

It takes more than a steep investment, however, to squeeze business value out of data. Companies have to establish an entire system to use data to drive competitive advantage.

Data as a competitive advantage needs a department that is responsible for the analytics and getting all the needed data.  The data owners and the data users should reside in the same division to ensure the right data is always available and up to date.  Also, the decisions on resources should be within that department, not within IT.

When IT is in charge of the data, they tend to not understand the business as well as needed to facilitate data.  The operations does not understand databases and technology, however the analysts understand the business and the technology, so they should own the data and the facilitation of the data.  

Manage Data with Organizational Structure

Article on who should manage data...​

most people management is actually done in the course of day-in, day-out work, by managers and employees. HR may very well define the semiannual performance review process, provide the needed forms, and make sure it is carried out. But performance assessment is completed by employees and managers.

This last point strikes at the heart of the federated model. Corporate HR sets policy; department HR may modify it in accordance with specific needs; and departments, managers, and employees carry out these policies. Most have a certain degree of latitude in how they do so.

 I am a big proponent of moving data management out of IT.  The HR model is exactly the model that works.  The business is closer to the data and very few IT department can handle the pace of the business when it comes to data management.  IT designs the network, builds the hardware and manages updates, while the business manages the ETL, data model and governance of the data.  

Will Data Science Become the New Bottleneck? - Forbes

many have posited that recalcitrant IT departments, hidebound by a history of rigid organization, have been a bottleneck to the adoption of new technologies and, by extension, the ability to distill business value from data.

We’ve also examined the potential and difficulties of analyzing big data, arguing that a new class of analyst, the data scientist, is on the rise in many organizations.

That may be part of the problem, rather than the solution.

I think this will be a major problem as big data moves into the mainstream.  So many organizations struggle with IT getting data that is readily available in the organization, wait until 20 groups are pinging 2 data scientists, who by nature are slow and go down unnecessary rabbit holes to find the truth.   

In reality, most businesses don't need all this data.  They need to perfect using their current data to drive actionable results.  Until they do that, there is no need to bring big data into the organization.  ​

How to Repair Your Data - Thomas C. Redman - Harvard Business Review

No matter what, do not underestimate the data quality problem, nor the effort required to solve it. You must get in front of data quality.

Data warehousing is hard.  To build a model that works for the business users and have data quality that truly delivers "one version of the truth" takes dedication and a group that truly understands the business.

Address preexisting issues.

 There are some problems that have been created already, and you have no choice but to address these before you use the data in any serious way. This is time-consuming, expensive, and demanding work. You must make sure you understand the provenance of all data, what they truly mean, and how good they are. In parallel, you must clean the data.

We are currently in the process of doing this in my organization.  In fact, we are going to rebuild the entire data model.  Sometimes it's easier to start from scratch instead of figuring out what is wrong with the current model.  Of course, our model isn't that wonderful for the business, so this made the rebuild decision quite easy.

Prevent the problems that haven't happened yet.

...build controls (such as calibrating test equipment) into data collection; identify and eliminate the root causes of error;

Data warehousing efforts also fail because end users find the errors most of the time.  When this occurs, getting the organization to trust the data becomes a challenge.  There is always the questioning of if this data is right.  Proactively fix data and let end users trust the data, they will spend more time discussing strategy instead of fighting over data quality.