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.

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™ 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 Tamir.Choina@maketfusionanalytics.com
Or
646-434-1005

The post Attribute Lift Model (ALM™) appeared first on Market Fusion Analytics.

The topic of personalization seems to be a ubiquitous one recently. Some companies have set out to develop platforms specifically dedicated to deliver a personalized experience, while others are incorporating it within their marketing journeys. McKinsey recently published an article stating that “personalization will be the prime driver of marketing success within five years.” While we don’t disagree that personalization can improve the end-to-end customer experience and as such generate better loyalty and empathy to the brand, there are factors that must be considered when thinking about personalization.

AI or not, it has been clear for many years that a personalized in-store experience and customization is what separates growing retail stores from declining ones. However, it is not one size fits all across industries. For some, personalization is of much greater importance than for others. It is more valuable when it comes to high involvement purchases like cars or personal items like apparel. As it relates to low involvement categories that have a very competitive and high density marketplace, the cost of change and personalization may not warrant the gains that brands will get from it.

As technology develops, some of these things become the cost of entry and elevate everyone in a similar way. As we all know, a point of difference drives competitive advantage. We may at some point shift our attention from people to brands and products. The focus has to go back to development of real innovation in consumer products and services, not marginal improvements. Those who figure out how to increase the success rate of innovation will win the battle at the marketplace.

The post Personalization For Marketing Success? appeared first on Market Fusion Analytics.

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.

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 Tamir.Choina@maketfusionanalytics.com
Or
646-434-1005

The post Power Pairs appeared first on Market Fusion Analytics.

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.

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 Tamir.Choina@maketfusionanalytics.com
Or
646-434-1005

The post Setting a Planning Posture in a Crisis and Post-Crisis Environment appeared first on Market Fusion Analytics.

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.

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.

If you are interested in learning more, please contact Tamir Choina at Tamir.Choina@maketfusionanalytics.com
Or
646-434-1005

The post ValueScores™ appeared first on Market Fusion Analytics.

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 info@marketfusionanalytics.com.

The post Building an Optimal Video Advertising Strategy Across TV and Digital appeared first on Market Fusion Analytics.

The post Store-Level Analysis Price Elasticity Estimates Are Biased Due To Shopper Store Switching appeared first on Market Fusion Analytics.