Offers that find the “right” customers?

Everybody talks about the right offer to the right customer at the right time.  While this is exciting, its also very difficult for most if not all… But Why? 

The overhead associated with testing and learning what offers work vs. not is the problem. 

The time, cost and complexity associated with testing a new or a derivation of an existing offer prevents the marketer from keeping pace with the sophistication and speed of customer behavior.  Typically, the total cycle time of learning extends beyond the pace of change of consumer behavior which can leave the marketer questioning “why even test at all”… 

What would happen if the overhead associated with determining the “right” customer and the “right” time to message were minimized or completely eliminated? 

A recent Toovio customer has experienced this dramatic reduction in overhead associated with fast learning.  As a result of the Toovio platform, this marketer was able to create more variation in offers and messaging thereby appealing to a broader base of customers.  We call this personalization “in the moment”…  a combination that spins an entirely new value proposition for the marketer as an exponentially increasing speed of learning is realized in the form of response and revenue optimization. 

Check out the case study here

Its time to flip the paradigm of traditional segmentation and targeting.  Let your offers find the customers within an ecosystem in which the “who” and “when” will sort itself out autonomously. 



How is Toovio different?

We get this one all the time and it just hit me this morning its actually a very simple answer.  In the past decade over 35+ implementations we've learned that real-time marketing systems only work well under certain conditions.  These conditions can be categorized into two buckets 

  1. Characteristics of the business model
  2. pre-existing decision component framework 

Toovio is different because we have learned a specific combination of conditions across the above categories that allow real-time marketing to flourish and we've built a software platform that focuses exclusively on these serving companies and customers under these conditions.  

Here's what we look for in a business model

  • at least 100,000 customers
  • customers making at least 2 revenue transactions per week
  • at least 50 offers, products or service message variations
  • a digitally known, opted-in customer that has a personal device  

What is a pre-existing "decision component framework" ? 

Most of the enterprise decision management tools today don't do much out of the box.  Meaning, when you install them you still need to configure them.  Here's where it gets complicated... How much configuration is necessary?  This depends on what industry you're in, what integrations you need, who the stakeholders are (if they exist) and what organizational goals and metrics they are accountable for...  When you start out with all these questions answered in a pre-defined way you can see results instantly instead of having to move mountains within your organization.  Pre-existing frameworks typically show consistent ROI within 1-3 months of go live whereas traditional tools can take as long as 8 - 18 months, if ever.

In this approach the main benefits that drive significant ROI are  

  • reduced IT costs
  • speed to market 
  • fast learning for the marketers resulting in exponentially increasing sophistication 

Toovio is different because we have a pre-existing decision component framework and we only apply it to business models that meet certain conditions.  We have proven this approach guarantees results and we'll stand behind our product even so far as to offer revenue share pricing. 



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.