Attribute Lift Model (ALM™)

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What product attributes (features, ingredients, claims, etc) are consumers willing to pay a premium for? Does the perceived value of the product change pre-COVID and during COVID-19? Market Fusion Analytics (MFA)’s proprietory Attribute Lift Model (ALM™) can quantify the willingness-to-pay for each, or a bundle of product attributes at different times.

A product could have more tangible attributes such as its pack size, form, packaging, scent, etc. The attributes could also be less tangible like brand or claims. Consumer willingness to pay for premium or other differentiated product attributes changes as the economic conditions change. The past recessions showed some premium features being price inelastic while others responding strongly to economic pressure.

Volume Decomposition Through ALMTM

In addition to measuring consumer’s willingness-to-pay, ALM™ can also isolate the incremental impact of a product attribute on overall in-market sale performance while controlling for price, distribution, execution, and in-market conditions. The incremental impact is measured as a % of lift over the “base” category item. ALM™ models are built using product (UPC) level scanner sales data and account for a) claims and attributes delivered to consumers currently in the marketplace, b) differences in distribution/merchandising across UPCs, and c) differences in brand, portfolio & category level impact.

ALM™ Process in a Nutshell

ALM™ offers strategic insights into the category footprint of white space potentials and forward-looking pricing and innovation planning. These insights are based on actual sales data and can be leveraged on a very granular level, by geography or even by account. 

If you are interested in learning more, please contact Tamir Choina at

Power Pairs

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Managing a large portfolio of products can be both a blessing and a curse. Companies need to understand ways to minimize portfolio interactions while increasing overall basket size. Market Fusion Analytics’ Power Pairs Analysis uses our Simultaneous Equation modeling approach to derive a brand/size interaction matrix. This provides guidance to minimize cannibalization and increase revenue by pointing out complementary brands.

Power Pairs are complementary brands that maximize portfolio sales when promoted together.

A typical Power Pairs Analysis examines a few years of sales data of dozens of unique brand/size pairs in hundreds of DMAs to help identify which combinations of brands should consumers be exposed to via promotion (marketing and discounting) that would result in the most incremental sales with the least cannibalization.

Power Pairs Analysis captures the complex interaction across brands/sizes and provides a true net impact on the portfolio.

Via the Power Pairs Analysis, a hierarchy of co-promotion by consumer preference type will be developed and distributed to both marketing and sales teams. The marketing team could leverage it to develop a communication strategy that might result in the development of a multi-brand campaign on TV or Digital/Social Video. The sales team could provide guidance to the field for trade calendar planning and execution by pointing out complementary brands to co-activate or harmful pairs to avoid co-promotion.

If you are interested in learning more, please contact Tamir Choina at

Setting a Planning Posture in a Crisis and Post-Crisis Environment

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As a result of the COVID pandemic, consumer behavior has changed across multiple dimensions – where consumers shop, how often, how much is bought and how much is consumed, at home vs out of home, etc. These changes along with the predictions of virus containment will influence categories and brands beyond the heat of the pandemic into the near future. Market Fusion Analytics (MFA) is leveraging its 20+ years of expertise in behavioral sciences to develop short-term and long-term category sales forecasts that account for changes in consumer behavior.

The causal models are built bottom-up by testing a rich set of predictors collected weekly at a granular level.

We combine our internal COVID-19 related projections using pandemic metrics at a granular geography level, data from multiple 3rd parties, and internal sales data to develop business forecasts which help our clients navigating this challenging environment. Our forecasts are derived from dynamic models and based on a set of assumptions that evolve over time as new information becomes available. We create an on-going data feed to track the accuracy of predictions and update projections to ensure better forecasting accuracy.

MFA’s Econometric Modeling isolates and quantifies the individual impact of multiple drivers of revenue sales, which includes the impact of COVID-19.

Our 2-prong modeling approach uniquely links predictions of overtime impact from COVID-19 based on MFA’s “Fear Factor” model and the omnichannel category sales forecasts. As a result, our forecasting models account for both category dynamics in the world of changing consumer behavior and the on-going impact of the COVID-19 pandemic on a specific vertical and sales channel. Unlike black-box models based on extrapolation, MFA’s “Fear Factor” model uses causal drivers such as the socio-political environment, density of population, demographics, social mobility, evolving consumer sentiments, etc., to predict the impact of the pandemic on specific category behavior at a local level. As a result, our data-driven approach provides an explanation (rationalization) for the projections which is key to validation and adoption.

If you are interested in learning more, please contact Tamir Choina at


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For years, the marketing industry has tracked brand equity through consumer surveys. However, more recent critiques of this approach have highlighted its inconsistency, unreliability, and inability to align with in-market performance. Part of the problem is that what consumers say is not necessarily what they do. Also, as it is often observed, brands on a downward trend retain loyal consumers that have a higher perceived value of the brand thus generating higher brand equity scores. In addition, survey-based brand equity metrics do not link directly to business levers and can’t be used to recommend specific actions to improve consumer-perceived brand value.


Market Fusion Analytics’ (MFA) solution to this problem is to utilize transactional data to measure brand equity. Every time a purchase is made, a consumer votes with their wallet. The greater the perceived relative brand value, the lower the demand elasticity and the stronger the brand. MFA believes that the analysis of observable consumer switching across category brands is the best measure of consumer-perceived brand value. Our framework is reliable as it is based upon millions of consumer purchases and actionable as it is directly linked to key business drivers.

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MFA’s alternative to traditional brand equity tracking is an innovative analytical product called ValueScores. ValueScores offers a deep assessment of key brand equity metrics and guidance on levers that help improve them.

Adding ValueScores to corporate decision-making ensures focus on enduring brand power and ultimately provides a path to profitable and sustainable corporate growth.

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If you are interested in learning more, please contact Tamir Choina at

Building an Optimal Video Advertising Strategy Across TV and Digital

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By Nazrul Shaikh

The landscape of when, where, and how frequently video advertising content is being consumed is changing rapidly. This feeds the need to rethink how video advertising content is planned and distributed. Though a lot of video advertising content is still being consumed over television (TV), an increasing amount is now being consumed online, and some content is being delivered both online as well as on TV.

TV generally offers higher reach and lower targeting while digital media channels offer higher targeting and lower reach. Now, there exists a continuum of targeting and reach that can be traversed using the right mix of TV offline and online media.

Market Fusion Analytics (MFA) has developed the means and methods to quantify this continuum based on identifying the differences in (a) retention rates and the need for frequency across channels, (b) support level that leads to the onset of saturation, and (c) the effectiveness of the same content presented to an audience over TV vs. online.

Our findings concluded that digital video advertising is in fact more efficient than TV. The reach is narrower and more targeted, driving greater sales per impression at lower execution costs, thus generating higher ROI. However, the efficiency decreases rapidly as investment levels behind digital video advertising increase. The impact of digital video advertising saturates early and companies need to account for these diminishing returns within their media strategy.

Given the narrower reach of digital video, the maximum potential from TV is significantly higher. For moderate to low levels of spend, digital video still proves to be more effective and efficient. To utilize digital video most effectively, companies need to spend on digital video advertising, but not exclusively. Rather than take a head-long plunge into digital, companies should develop a media strategy that balances investment in both TV and digital video to reach full potential.

While corporations and advertising agencies are debating the split of their media budget by channel (i.e., TV vs. digital, and within digital — display vs. search and social), we argue in favor of a split that gives more weight to the different tactics to reach a consumer, i.e., videos, banners, incentives, keywords, etc. and treat the channels as a medium of delivery to control for reach and frequency.

For help with optimizing your media mix, reach out to us at