Bridging Digital + Physical consumer experiences

For a lot of us, the speed at which technology advances can seem to alienate people rather than bring them together. Certainly, in the world of B2B marketing software there is room for improvement with respect to bringing people together.

As human consumers, we hold brands accountable. Every interaction is judged. Every touch point is reflected upon as time passes. We continue to be a customer as we conclude the previous interactions positively contribute to our lives.

As individuals, we all make decisions and reflect on those decisions differently. Think about these decsions as the data we use to navigate the most human of processes: choice. This data includes your aspirations, goals, interests, state of mind, circumstantial constraints, and so on.

For decades, Marketers have attempted to infer these choices from behavioral data that occurs before a choice is made and becomes irrelevant. Worse yet, as consumers reflect upon their choices, the inferred data becomes more inaccurate or even harmful. In some industries, inference via behavior data is acceptable and relatively accurate, however, for most industries, inference is simply unattainable given the nature, quality or timeliness of behavioral data.

To evolve the ways consumers and marketers are connected we must evolve the shared data set they mutually leverage within their interactions. In my humble opinion, this connection absolutely begins with transparency. Transparency between the brand and consumer that the brand is available, listening and prepared to engage in a digital dialog, which will ultimately result in a remarkable experience.

Dialog is the key to bridging digital + physical experiences. Dialog from the consumer’s perspective is communicating what they want to achieve: aspiration, goals, interests, circumstantial barriers, etc. Dialog from the brand owner’s perspective is the recognition (in a real-time environment) of the following:

  1. The consumer’s profile, i.e. who they are
  2. How their profile compares to all other profiles
  3. Subsequently, how the brand owner strategically values said profile in that moment in time

If both consumer and brand owner are able to achieve this dialog in real-time, the experience can be remarkable by shared dataset from which the organizations’ content & incentive can be delivered.

If the dialog was transparent and delivers as promised, then the consumer and brand owner can mutually choose to persist the shared data within the interaction across digital + physical experiences. The consumer must have the choice to engage and continue to engage. Equally, the brand owner must deliver on the promise of appropriate valuing the consumer’s profile against all other profiles.

As consumers, we are digital, we are smart and we are equipped with an ever increasing number of choices. Those brands that acknowledge this sophistication and open dialog during the interaction shall be handsomely rewarded.

Big Data: Value from a Marketers’ Perspective

I’m a big fan of data and the geek in me enjoys the typical research we all see out there today on Big Data with respect to the 3 Vs. The graphic below shows the interaction between the 3 Vs of big data: Volume, Velocity & Variety.

But what do the 3 Vs mean from a marketer’s perspective? Unfortunately, I tend to think that for the marketer the 3 Vs present more questions and challenges for a marketer rather than provider answers or solutions.

For the marketer interested in acquiring, growing and retaining customers across channels they need to intimately understand Big Data. While the 3 Vs are helpful in describing the nature of Big Data we need to apply an entirely new set of evaluation criteria to properly value Big Data.

Real-time marketing is all about making customer experiences (i.e. communications and or content) RELEVANT, CONSISTENT and CONTEXTUAL. The utilization of Big Data should be based on wether it is Relevant, Consistent and Contextual. If it is all of those things it is highly likely to contribute to a customer lifecycle strategy across channels.

In line with a CRAWL-WALK-RUN approach, a sure fire way to begin this evaluation is to create your own data experiment. Meaning, forget VELOCITY and VOLUME and just focus on VARIETY. Do this by sampling the volume down and ignoring where and how quickly it would need to go. Design a test environment where you can simply collect and manipulate all the data in one place. Then, play theoretical games with customer experiences without regard for technology. Ask yourself the question do you have the data or not? If you think you have the data then apply the criteria of relevancy, consistency and context.

If you pass this exercise with flying colors then its time to call your IT partners! Having customer experiences thought through at this level of detail goes a long way to being able to articulate the value of Big Data and certainly will contribute to justify cost of any technology or integration solution to follow.

Real-Time Marketing Defined

For the marketer, the ability to test, learn and react with your marketing content & initiatives in a contextual, relevant and consistent manner irrespective of how diverse and frequent multi channel consumer channel interactions occur.

That’s a mouthful but the important distinction between traditional marketing and or marketing automation and real-time marketing is really about the evolution of the customer and NOT the evolution of marketing strategies or tactics.

Meaning, the evolution of customer expectation and sophistication. Consumer sophistication is evolving at an exponential rate because of the accessibility of technology, information, choices etc. This in turn raises the level of consumer expectation. This new challenge for the marketer can only be resolved by implementing the definition of real-time marketing (above).

While many companies like to think that their customers can be neatly sorted into various demographic, relationship and lifestyle buckets. The reality is that customers are unpredictable entities who possess a wide range of differing wants, needs, interests, behaviors, preferences, and other characteristics. As a result, the best-in-class companies strive to understand customer behavior at an individual level and to act upon that understanding to delver the most relevant messages, offers and service treatments at the “moment of truth”.

In order to enable this type of responsiveness, companies must first shift their customer interaction mindset from traditional outbound communications to embrace more sophisticated inbound marketing strategies, techniques and tools. A real-time marketing program will enable the successful migration from an outbound centric organization to a best-in-class enterprise focused on inbound interactions that drive more profitable and loyal customers.

Top 3 Real-time Marketing Analytics Initiatives

I’m a huge fan of analytics… so long as they can remain actionable. You hear the term actionable data insights quite a bit these days. Well, for me it actionable means the insight applies to or results in a tactical impact to marketing initiatives.

The umbrella of analytics as it relates to marketing is voluminous and confusing to say the least. The nature of analytics as a practice and skill is by definition a platform that can “do anything”. Having been a part of many teams in my own past that deliver analytics to support marketing initiatives I can say with confidence that any analytical project needs direction. A data scientist might call this direction a hypothesis statement.

Of all the possible ways analytics can impact marketing initiatives, as a marketer, I think there areas 3 to focus on more so than others that will yield ROI.

1. Customer Segmentation

Foundational in nature, a strong customer segmentation is key in the test, learn and react cycles for marketer’s specifically for measurement of ROI. Encourage your analysts to build less than 10 customer segments that are based on data attributes that are:

  • Easy to understand
  • Stable in that they are present (i.e. populated) for the vast majority of customers in the database
  • Accessible

2. Offer Mix

What offers are available, who are they being presented too, in what channel and why… In the very first tests the offer mix may seem subjective in nature but remember to ensure that a “size of prize” analysis is done to understand the following:

  • How many customers are eligible for each offer
  • What does each offer cost to serve / fulfill
  • What is the gut feel for response rates for each offer
  • What is the ROI of the offer

After the first few tests come back with results analytics can step in with all kinds of algorithms and methods to optimize the mix, it may not seem like it but this is actually the easy part. The challenge is to structure the hypothesis statement or give the appropriate direction. Here are some guidelines to use, as a marketer, when giving direction to analyst on optimizing offer mix:

  • Define success metrics explicitly
  • Delineate short term success vs. long term success
  • Define “what if” parameters in your strategy for the analyst

At a minimum if you give this direction you’ll end up with a platform of analysis that produces enough results to facilitate test, learn and react cycles that will increase ROI.

3. Long Term Impact of Offers

Driving organizational value from marketing programs is a game of inches. Every once in a while you have to pick your head up from the day to day rigor and look at the scoreboard. Here are a few best practices:

  • Ensure you create and maintain a universal control group
  • Regularly (perhaps quarterly) analyze movement in your customer base. Who’s new, who’s leaving and who’s shifting
  • Establish an internal benchmark for long term performance as well as an industry benchmark to compare against

The analysis conducted in this area of focus is almost certainly a marketer’s most valued product delivered to their organization.

- Josh Smith -