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Case Study - Krusteaz

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The Challenge

A leading global provider of packaged foods needed to determine if new packages would improve sales. Previous package-testing research of the traditional kind was inconclusive.  We needed more penetrating research that would answer WHY questions. We needed to determine why some packaging was successful, and another packaging was not. We also needed to prove to the client that our conclusion was not opinion but was backed up by hard data based on their consumers’ biological reactions to their packaging. We needed to prove to our clients that we could deliver the information they needed and develop a method of testing that would satisfy them.

The greatest challenge, as one of our team, put it, was "We had to produce deeper insights into why consumers were not attracted to the new package designs with only a limited budget." We needed to do more work and determine why the packaging was not attracting consumers and do it with the least amount of money we possibly could.

Our Solution

Step one was to fully understand why the new packaging was not appealing to the consumers. What exactly did the consumers see visually that was causing them to reject the product? Additionally, we needed to find a way to test consumers without relying solely on what they had to say, as this had provided inconclusive answers in the past.

We needed to determine if our client's new packaging would improve sales and why their previous packaging had failed and proved this with scientific evidence eschewing anecdotal data for quantitative analysis and hard numbers. This type of information on packaging is difficult to quantify, so we had to design a test to give us the data we needed.

We chose to use biometrics in the form of neural-wave and GSR (Galvanic Skin Response) measurement. In this way, we didn't have to rely simply on what the consumers have to say about the packaging; we could analyze their reaction in real-time and accurately, even when their bodies disagreed with what they had to say. No matter what they said, their bodies always told the truth.

Impact for Client

The client was properly warned that launching the new packages would lose $19 million in the first year, and the biometric method gave us clear answers as to why. The data that we could give them saved them a disaster in public relations and a fortune in money. We were able to take quantitative data and biological analysis that led to actionable activities to improve their design. We were able to work closely with the client, partner with them, and understand exactly where their pain points were, and in the end, give them evidence that their new packaging would not work.

The client was pleased and said, “Matters has opened our eyes to a new approach to consumer input in our package design process. Their approach to neuroscience methods has allowed us to have more confidence that the consumer reaction we see is a true reaction of their subconscious, rather than a rationalized, linear reaction that is not reflective of behavior in the marketplace. The ability to see neural lifts correlated with purchase gives us a real-world understanding of the effect proposed package designs would have on our business. In addition, the Matters team has been extraordinarily generous and hands-on, building the expertise of our team in the field of neuroscience research; their time spent training and coaching us on how to interpret the results has led to increased excitement and actionability coming out of the work."

Case Study - UC4

UC4 logo MED

Improve Digital Channel Lead Capture Performance

The Challenge

A leading global provider of IT infrastructure automation solutions needs to strengthen its U.S. presence. The woefully underperforming program needed to produce leads or risked being eliminated. Lead generation volume from online was low and cost per acquisition was excessive.

Despite superior technology, deep-pocketed competitors dominated the market. Messaging which supported specific environments for Oracle, SAPVMWare platform support was not reaching the audience effectively. Available time from the Marketing Programs Manager was limited and occupied with other projects.

Our Solution

Step one was to fully understand the value proposition for UC4’s audience. What pain points did UC4 address and who were the buying influences involved in this complex enterprise software and service sale? Additional effort was invested in learning the key features for the most influential buyer personas. 

Based on our research the pay-per-click program architecture was rebuilt targeting messages that would resonate with key influencers. A keyword gap analysis was executed to identify holes in targeting. Website analytics were used to identify natural search trends and content most sought by visitors.

A complete assessment and overhaul of the digital marketing strategy were conducted and revisions implemented. Display media and retargeting were introduced and tested proving a valuable new ad distribution channel. Landing pages were A/B tested, optimized, and integrate with UC4’s lead nurturing platform resulting in more sales qualified leads (SQLs).

Impact for Client

In the first 6 months, lead acquisition cost was cut by 55% and lead volume skyrocketed up 156%. From when The Matters Group (then Confluence Digital) began, to the most recent completed quarter, the conversion rate was up 245%. Lead volume is up 20x and cost per lead acquisition is down over 80%. In an analysis, nearly $1 million in revenue was traced back to the program managed by The Matters Group delivering nearly a 10:1 Return on Investment. After years of modest growth, UC4 enjoyed a ramp in new clients and revenues which were parlayed into an acquisition by CA for $600 million.


UC4 Matter Group results

Case Study - Blue Cross Blue Shield

Advanced Machine Learning Deployed to Mine Insights

The Challenge

Two large, regional Blue Cross / Blue Shield organizations urgently needed to learn about new members who came in through the Affordable Care Act (i.e. ACA or "Obamacare") - what were their likely future care needs, their payment resources or shortcomings? 

Organization 1 - saw themselves already losing millions and called on an ACA provision that allowed them to sue the federal government for repayment of losses. 
Organization 2 - tried a different approach- remediation, ie., identifying high-risk customers and negotiating agreements to reduce likely losses. 

The big question was how to identify those customers? How could they manage the flood of data pouring in with new applications? - the answer..Machine Learning 

Our Solution

The answer was in developing an artificial intelligence model that incorporated machine learning. We developed a highly advanced solution that leveraged machine learning for each Blue organization.
In insight 1: We were able to identify those few among the masses that were new applicants and had a higher potential profitability. To do so we:

    • Determined who were and would be active consumers of provider services.
    • Who brought high net value to the the client, as defined by dollars paid in premiums minus the predicted cost of providing services. 

In insight 2: Among the millions of new applicants eligible for health care, the model identified those whose cases promised the most immediate positive results and put them at the head of the queue.

Impact for Client

The specific financial impact is subject to a non-disclosure agreement, but the client has been able to profitably remain in the market delivering health insurance options for nearly a decade. Additionally, provisions in the ACA allowed for firms to take legal action against the government if the provider sought to be released from the obligation to provide services. A long protracted lawsuit would have been extremely expensive and tie-up important company resources. Being able to deliver a better solution for the market without resorting to litigation saved millions in legal fees while adding a new revenue channel.

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