Closing the Loop on Marketing Automation

Marketing Automation is starting to come into the mainstream, but many companies are not using the toolset to create amazing interactions with their customers.  I know many brands that have sophisticated toolsets and it is used to show me points and my name.  I get the same exact email that all of their customers get with my name attached, this is not an amazing interaction.

Data and Analytics as the Foundation

Seems logical, right? You would be amazed at how many brands are still working through “We don't know how to get our transactional point-of-sale integrated with our demographic and third-party purchase data.” Solid data management and extract, transform, load processes form the foundation from which a solid enterprise marketing platform is built.

Data is the backbone of any marketing automation solution.  This may be why the technology isn't as pervasive as it should be.  Getting all of the data into one location in a consumable format for marketers is not an easy task.  This is the first step in the marketing automation journey.  Starting with the data will increase the chance to have amazing interactions with your guest.  The more knowledge about the customer, the more customized a communication can be and this is what delights the customer.

Integrate, Orchestrate and Optimize

This is a large category, but an important one as far as customer engagement is concerned.

First up is integration. Integrate marketing programs across channels — leverage insights from outbound marketing programs to better serve customers on inbound channels and vice versa. With consumers switching channels as frequently as they do today, this is imperative.

Marketing automation tools today can currently run many inbound tasks.  This is especially true when sub second response is not necessary.  When giving the customer an option to to click on a button to serve up an offer or promotion, use the marketing automation tool to serve up the offer.  This way it ensures the customer is seeing the same offer they saw in an email you sent yesterday.

Orchestrate campaigns and their offers so that the timing and sequence, as well as the channel delivered, make sense based on individual consumer preferences. 

So many brands are tied to their own timing of communication, not the customers.  For instance, we alway send our bi-weekly communication on every other Tuesday.  This makes it easy for the marketer, however this does not take into account the customer.  

All customers should be on their own timeline.  Marketing automation tools are very sophisticated and can handle this type of philosophy.  Planning the interactions with customers based on their behavior will result in much higher response.  This is the type of delightful interaction customers expect.

Optimization is the final step in the execution phase. Make sure you use analytically based optimization across all channels to avoid over-contact and saturation of consumers. Consumers are only annoyed by receiving an email offer for a product or service that they just signed up for last week during an inbound contact center conversation.

Be sure to optimize constantly.  Marketing automation campaigns are living and breathing entities.  They are never finished and there is always money to be found in optimizing the programs.  Optimization goes much further than over contacting the customer, just as bad is not contacting the customer at the time they want to purchase.  Even worse is offering the customer something they would never be interested in, especially if they have been your customer for an extended period of time.  Tiffany, I already bought my wife the diamond, stop telling me about how amazing it is, you had me at hello.

The last step is to close the loop in order to perform truly integrated marketing. Take the information you learn from the delivery of both inbound and outbound offers: Did a customer open an email, respond to a social message or accept a verbal offer delivered via the contact center? If so, what effect does that have on downstream marketing efforts?

It all comes back to constantly learning.  The more your customer interacts with the brand, the more they tell you about themselves.  I am not the biggest fan of over surveying the customer and when asked why that is, I say its because I survey my customer all the time.  I send them outbound communications with call to actions and if they reply, they are telling me what is more important, voting with their wallet.  If they don't reply, they are telling me they don't appreciate this offer, or maybe it is this time, etc.  Learn from these interactions and enhance your campaigns.

Remember, Data + Insight = Action.  Always be looking for actionable data on your customers and using that in your marketing automation programs.

Data + Insight = Action and Back Again

Adobe Summit brought with it a great nirvana of a near-future where marketers are able to deliver relevant content to customers creating great experiences.  The words that permeated throughout the conference were those, content, experiences and data.  The big stars of the show were content and customer experiences, which I believe are extremely important as I have written before.  

However, there is no right content delivered at the perfect moment to create wonderful customer experiences without data.  Data is the key to making this all work, and not just any data.  No, I'm not talking about "big data", I'm talking about actionable data.  

Most companies are sitting on a treasure trove of data already.  Without purchasing third-party data, understanding every click, customers have data that can transform their business.  The issue is in interpreting the data, making it actionable.  Actionable data isn't a product, it's a culture.  

Actionable data is the combination of art and science.  The path to actionable data isn't necessarily going out and hiring a bunch of talented data scientists, though it doesn't hurt to have these people on your team.  The path to actionable data is marrying the data with the business acumen.  It's not enough to have data telling you something happened, there has to be an understanding of the business as to why it happened.

Once there is an understanding of what happened (science) and why it happened (art), you have actionable data.  Now you can create optimal tactics to deliver relevant content to create targeted experiences in the digital age.  The great thing about this process is it's circular.  Once a company creates great targeted experiences for their customers, customer behaviors will change and the entire process starts all over again.  There are always puzzles to solve and amazing content and experiences to create.  

Is Data Visualization Actionable?

Although data visualization has produced some of the most captivating artistic displays in recent memory, some of which have found their way into exhibits at the New York Museum of Modern Art and countless art installations around the world, business leaders are asking: is data visualization actionable?

I believe for the most part, the answer is yes.  Data visualization tells a compelling story which allows the consumer of the data to see patterns that are missed in analyzing the numbers.  Even when visualization cannot tell the whole story, they are able to point you in a direction that will save countless hours as they give a great starting point.  

The other side effect of visualization is it engages executives.  Executives love pretty pictures and stories without having to dig through multiple pages or large spreadsheets of numbers.  Dashboards are all the rage, but the visualization tells a story that executives can appreciate. 

FiveThirtyEight's Nate Silver Explains Why We Suck At Predictions (And How To Improve) | Fast Company

When human judgment and big data intersect there are some funny things that happen. On the one hand, we get access to more and more information that ought to help us make better decisions. On the other hand, the more information you have, the more selective you can be in which information you pick out to tell the narrative that might not be the true or accurate, or the one that helps your business, but the one that makes you feel good or that your friends agree with.

This is a great article on using data and predictions.  I just bought this book as a good friend of mine suggested it is a great read.  I always hear "You can make numbers tell whatever story you want."  Ain't that the truth?  So many times colleagues of mine hold on to a certain part of the data that tells the story they want to tell and soon it becomes truth, however this only helps them look good instead of moving the business forward.  

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.

Big Data's Human Component - Jim Stikeleather - Harvard Business Review

Machines don't make the essential and important connections among data and they don't create information. Humans do. Tools have the power to make work easier and solve problems. A tool is an enabler, facilitator, accelerator and magnifier of human capability, not its replacement or surrogate ... That's what the software architect Grady Booch had in mind when he uttered that famous phrase: "A fool with a tool is still a fool."

From my last post, I talked about humans being able to make the data actionable.  The understanding of the data that is used for the model is more important than understanding the ​math behind the algorithms.  Algorithms can find the patterns humans can't, however the algorithms can't determine if the answers are relevant. 

We forget that it is not about the data; it is about our customers having a deep, engaging, insightful, meaningful conversation with us

Exactly.​

Understand that expertise is more important than the tool.  Otherwise the tool will be used incorrectly and generate nonsense (logical, properly processed nonsense, but nonsense nonetheless).

The answers will be fancy, but will not help make decisions for frontline or CRM more effective.​

When we over-automate big-data tools, we get Target's faux pas of sending baby coupons to a teenager who hadn't yet told her parents she was pregnant, or the Flash Crash on Thursday May 6, 2010, in which the Dow Jones Industrial Average plunged about 1000 points — or about nine percent.

Humans should always be paying attention to the outcomes and put parameters around the use of automated answers.  Answers should be used in conjunction with other factors for the best decision.​

Although data does give rise to information and insight, they are not the same. Data's value to business relies on human intelligence, on how well managers and leaders formulate questions and interpret results. More data doesn't mean you will get "proportionately" more information. In fact, the more data you have, the less information you gain as a proportion of the data (concepts of marginal utility, signal to noise and diminishing returns). Understanding how to use the data we already have is what's going to matter most.

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What Executives Don't Understand About Big Data - Michael Schrage - Harvard Business Review

too many organizations don't quite grasp that being "big data-driven" requires more qualified human judgment than cloud-enabled machine learning.

Human judgement also comes with a caveat, the human's have to be knowledgable about the business at hand.  So many times consultants come in and use terms like "big data" and "data modeling" with promises of transforming the business.  Much more goes into transforming the business with data and that is the knowledge to take the findings and apply those findings into the CRM efforts to make an impact.

What works best is not a C-suite commitment to "bigger data," ambitious algorithms or sophisticated analytics. A commitment to a desired business outcome is the critical success factor.

The desired outcome is so important.  Otherwise the result is usually not actionable, just informational.  ​

Executives need to understand that big data is not about subordinating managerial decisions to automated algorithms but deciding what kinds of data should enhance or transform user experiences. Big Data should be neither servant nor master; properly managed, it becomes a new medium for shaping how people and their technologies interact.

Without taking the findings and enhancing decisions made by frontline staff or database marketing experts, the data is not actionable. ​

Marketers Flunk the Big Data Test →

This is a great article on making data actionable in marketing.  This is the toughest part of Customer Intelligence.  Creating reports is so much easier.​

On average, marketers depend on data for just 11% of all customer-related decisions.

This is a disturbingly low number, but one that does not surprise me.  ​

But in today's volatile business environment, judgment built from past experience is increasingly unreliable. With consumer behaviors in flux, once-valid assumptions (e.g., "older consumers don't use Facebook or send text messages") can quickly become outdated.

In my industry what worked 10 years ago has no bearing on what works today.  Even what worked before the recession does not translate.  What your customers are doing today is more relevant than what they used to do.  ​

When we tested marketers' statistical aptitude with five questions ranging from basic to intermediate, almost half (44%) got four or more questions wrong and a mere 6% got all five right. So it didn't surprise us that just 5% of marketers own a statistics text book.

Marketing analysts don't even have statistics backgrounds.  Analysts who have SQL background and can get data are usually the ones that shine, however they usually don't make the best analyst.  Analysts are supposed to analyze data, not pull data.​

...dashboards often capture response-based metrics such as clicks and open rates that aren't tied to more important measures such as customer loyalty or lifetime value — and yet, marketers are rewarded for improving the response metrics.

Defining the right metrics are so important.  Marketers have to look at the long-term value of guests and resist the temptation to micromanage individual campaigns.​

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