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Case Studies - Business Problems Solved Using Research - elucidate

Case Studies

Sometimes seeing how we specifically have helped clients is the best way to see if we might be a good match for you. Below are a number of examples of client successes.

A Product Design Firm Needing a Quantitative Research Partner

Client/Background:

A product design firm with qualitative research expertise asked us to support a project for a large consumer electronics firm. The goal was to combine qualitative and quantitative findings to answer questions about tablet use. Based on poor previous experience, the client was concerned about awarding the project to two separate firms, and wanted some assurances that there would be a unified approach.

Business Problem:

Perform a quantitative survey that validated qualitative findings and drilled down into specific topic areas.  Provide integrated solutions/recommendations for the end-client.

Our Solution:

In order for us to be as integrated as possible, we were heavily involved in the project proposal and sale, we provided feedback on the qualitative instrument, and we attended some of the qualitative sessions.

While analyzing the findings, we first completed our analyses. We then had multiple working sessions, where we focused on identifying findings that were consistent across the qualitative and quantitative work, and noted findings that were contradictory. During working sessions, every client business question was addressed through a combined view of the qualitative and quantitative work.

Outcome:

It was clear prior to even presenting the data, that we had discovered some very valuable conclusions. Presenting findings to the end-client confirmed this value.

The design firm looked good in the end-client’s eyes, and a strong partnership was developed.  It is clear that the end-client will reach out to this partnership for combined qualitative/quantitative research projects in the future.

A Medical Device Manufacturer Needing to Price a Device and Its Consumables

Client/Background:

A $1B division of a global medical device manufacturer needed to understand how specific price changes might change the demand for the product. Given the sales volume of consumables associated with the device, changing price by a small amount could impact bottom line of the company.

Business Problem:

The manufacturer wanted to understand the optimal pricing for hardware and consumables for the core product line. In addition, the client wanted to understand differences in preference between buyers and end-users, because they were thought to have unique needs and price sensitivities.

Our Solution:

The client originally considered another methodology, but after discussion, agreed to our solution, which was a better way to get at pricing and additionally offered insight into feature preferences. We used Adaptive Choice-Based Conjoint Analysis to address the client’s needs. We also used individual-level modelling to efficiently understand differences between end users and buyers.

Outcome:

The project success led to:

  1. Repeating project in other countries
  2. Being asked to execute additional research with other units
  3. Asked to be consultants on initiatives (pre-research) to provide guidance on product roadmaps

A Television/Telecommunications Company Needing to Identify Vulnerable Customers

Client/Background:

A telecommunications company working with another marketing research company asked us to develop a customized methodology to identify vulnerable customers.

Our Solution:

We used a proprietary solution that helps identify customer churn before it happens.  We asked high level loyalty questions as is typical with customer satisfaction surveys. However, we also presented competitive market options to current customers.

In showing these competitive offerings, each customer was asked which products were strong enough to tempt them to defect from their current supplier, and which product features most affected their decision. In order to do this we provided a modified Adaptive Choice-Based Conjoint methodology and combined it with a Reverse Segmentation.

Analysis allowed us to provide the proportion of the customer base that was vulnerable, the customers’ profiles, and recommendations for retention.

Outcome:

While we were initially brought in to provide the custom methodology, both the client and the market research company relied more and more on us as a resource on the project, where we ended up leading the project.

Recommendations made it all the way to the CEO, and results were shared on quarterly earnings call.

An Office Product Supplier Needing to Optimize their Products across Three Unique Markets

Client/Background:

One of the world’s largest office product suppliers came to us wanting to run a conjoint project for one of their products.

Business Problem:

The project evolved into a conjoint analysis incorporating three separate markets, six competitive brands, and 36 products that contained up to 15 attributes each. Due to the large number of attributes, a custom methodology was used to accommodate the large number of attributes.

Additionally, the client had a negative experience using off-the-shelf market simulators and wanted a customized simulator.

Our Solution:

We designed an Adaptive Choice-Based Conjoint methodology that used a pre-screening method in order to accommodate all 15 attributes.

We delivered results that identified our client’s share of preference for products a number of ways. We showed the products share of preference: within its unique market, the total client share in each market, the total competitor share in each market. We also recommended improvements that could be leveraged and used across all three markets.

Finally, we created a customized market simulator that met the needs of various stakeholders.

Outcome:

After delivering recommendations and completing the simulator training, the client felt that the project was a complete success. This feedback was also delivered from business stakeholders who continue to use the simulator provided (almost 1 year after completing the project).

A Consumer Electronics Company without a Researcher Department

Client/Background:

A new head of brand strategy for a large consumer electronics manufacturer needed to create a process she could use to measure current brand effectiveness in order to impact of new strategies going forward. The company did not have any in-house research staff, so she asked us to become involved. Our client’s focus was on getting data to answer business questions, and she did not have the time to discuss the details of the research process.

Business Problem:

The client wanted to understand how its brand is seen in established and emerging markets.

Our Solution:

We critically refined brand attributes to ensure that they were unbiased and concrete for respondents. We ensured appropriate competitive brands were included to allow for brand mapping of the entire ecosystem.

We employed a methodology that could rank the attributes in terms of objective importance to purchase consideration.

We provided a brand mapping tool that gave a three dimensional view of the company’s and competitors’ relative strengths and weaknesses.  Anchored MaxDiff ranked the importance of attributes to purchase consideration.

Outcome:

The client was very pleased with the process and our ability to communicate results using business language rather than research terminology.

The trust our client has in us has led to multiple projects and presentations with executive leadership (including the CMO and CEO).  We are seen as a resource with which business problems can be discussed well before the development of RFPs.

An Automotive Company Wanting to Optimize Features and Price

Client/Background:

An automotive company commonly runs “clinics” where target customers view new concept vehicles among other “de-badged” competitive cars.  Beyond the qualitative information they collect during this exercise, the company wanted to expose respondents to a trade-off exercise incorporating features and price.

Business Problem:

Optimize the price and feature set for a new vehicle.  Understand price sensitivity and the incremental value associated with particular feature enhancements.

Our Solution:

A conjoint exercise was designed and placed on computers in the clinic environment.  Respondents first viewed vehicles live, then images of the vehicles, along with specific feature call-outs, and prices were designed into a choice-based conjoint exercise with conditional price adjustments.  Conditional pricing was necessary to accommodate the differential price effects of similar features based on body type, trim, etc.

Hierarchical Bayes analyses allowed limited sample sizes while still producing useful models.  A what-if market simulator was created to allow feature ad price optimization within the context of a likely competitive consideration set.

Outcome:

The project success led to positive feedback from the product development team regarding the usefulness of the results, money savings due to more focused development strategies, and repetition of this project methodology for other vehicle scenarios.

A Hardware and Services Company Wanting to Customize Messaging to Different Groups

Client/Background:

A $1B consumer electronics manufacturer that provides hardware and service in the television market was looking to provide customized messaging to unique groups of customers and prospects.

Business Problem:

The client wanted to identify unique groups of customers and prospects based on feature preference, value propositions that resonated with them and likelihood to buy/upgrade, in order to better target them with appropriate messaging and products.

Our Solution:

We determined feature preference and resonance with value proposition using an Anchored MaxDiff methodology.

We then combined the Anchored MaxDiff scores with targeting variables and likelihood to buy measures in a Reverse Segmentation analysis. This allowed us to create segments with unique needs and values and with unique targeting profiles.

We then created a segment Priority Scoring system to guide resource allocation.

Outcome:

The client continues to use the segmentation solution we provided and refreshes feature preference work on an ongoing basis.

The client has come to us for other projects, often more complicated ones that requires combining methodologies into unique solutions.

A Software Company Wanting to Identify the Best Prospects for Sales Efforts

Client/Background:

A software company with access to a large database of prospects had traditionally sorted prospects based on simple identifiers such as company size and revenue.  They approached us to help them improve their targeting precision so that they could better allocate resources to prime targets.

Business Problem:

Improve targeting to focus on prospects most likely to convert to customers, and to become high revenue-generating customers.

Our Solution:

Predictive modeling work necessitates a solid upfront understanding of explicit goals, and of the data available to create models to achieve those goals.  We spent a number of calls early in the process to ensure that the project path would accomplish these goals.

When it came to the modeling, we explored a number of different potential modeling solutions using a variety of potential input variables, until we reached a solution with high predictive accuracy and flexibility to allow the client to be more or less conservative in who they targeted.

Scoring algorithms were created to tag all prospects in the client database, and which will allow scoring of future prospects as data is updated.

Outcome:

The client is successfully using the models to focus their sales staff on most-likely convertible prospects.

They are exploring the possibility of creating additional models focused on additional products from their line-up.

A Hardware Company Wanting to Optimize Their Product Features and Prices

Client/Background:

A privately held streaming television hardware company came to us to optimize their products.

Business Problem:

The client wanted to configure their line-up of products with the optimal features and prices to maximize revenue and avoid cannibalization.

Our Solution:

We designed an Adaptive Choice-Based Conjoint exercise to include key attributes of all relevant products.

We used summed pricing methods to provide thousands of price point inputs. The design was set up to be a realistic purchase exercise for respondents. Also, the design provided for very precise price modelling on the back-end.

We also created a market simulator to model various potential line-ups and prices until the optimal configurations and line-ups were discovered.

We included flexibility in the design to be able to include competitive offerings and understand their effects on line-up choices.

Outcome:

The research led to concrete recommendations on core pricing and configuration. Business decisions were impacted by the research outcomes.