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MESH’s experience data improves the conventional MMM approach

MEDIA RELEASE: November 22 2021: MESH Experience is delighted to reveal how the use of their experience data improves the insight from advanced marketing mix modelling, as demonstrated in a recent publication by Dr Peter Cain of Marketscience in the International Journal of Research in Marketing (IJRM).

With the advent of digital media, modern marketing mix modelling typically focuses on consumer journey models of demand, where customers are exposed to a sequence of touchpoints on the path to purchase. It is important to understand every single touchpoint along the journey, from seeing a TV ad to spotting the brand in-store or speaking to a friend about it. However, it is difficult to find a data source that includes every touchpoint. The MESH Experience Real-time Experience Tracking (RET) approach, described in Harvard Business Review solves this problem and brings a new dimension to marketing mix modelling.

MESH’s experience data improves the conventional MMM approach in three main areas:

  1. Creates consistent media metrics for improved comparison
    MESH’s experience data enables the creation of GRP-type metrics for all   marketing variables, allowing for more robust comparisons. Previously, comparisons were complicated by marketing variables expressed in different units such as GRPs for TV advertising and clicks or impressions for paid search.

  2. Facilitates measurement of marketing tactics
    MESH’s experience data allows us to adjust the quality and reach data to enable sufficient variation for robust time series modelling.  For example, the role of in-store product display is often a constant presence lasting many months creating a clear measurement challenge in marketing response analysis. In-store customer experience can provide necessary variation, where constant presence is combined with the quality of the encounter and the degree of customer interaction.
  3. Helps to explain long-term purchase patterns
    A key part of the marketing mix model is an estimate of base sales. This represents the long-run or trend component of the data, reflecting any persistent changes in consumer brand loyalty. Conventional mix models typically assume a fixed or deterministic baseline. As such, estimation of customer loyalty and the long-term impact of marketing is precluded by construction.

    In the Marketscience approach, base sales are modelled as an evolving time-varying process in tandem with short-term effects. This enables quantification of the drivers of base sales, providing a deeper understanding of the mechanics of brand-building.

    Customer product and brand experience play a critical role in this process, helping to explain baseline evolution as part of the earned media experience.

Dr Cain, Marketscience partner and co-founder, says, “Providing more clarity around the real science underlying marketing analytics is critical in these days of increasing focus on measurement. Incorporating MESH’s experience data into our marketing mix approach plays an important role, helping to explain short and long-term marketing effectiveness.”

MESH Experience Founder and President, Fiona Blades, commented, “I’m excited to see our experience data contributing to improving marketing effectiveness. It’s all the more compelling that it provides credibility for CFO’s.”

MESH Experience’s unique approach aims to help clients see the world through the customer’s eyes. The company collects and analyses data in real-time across the full spectrum of what people experience about a brand – all paid, owned, and earned touchpoints – allowing brands and campaigns to be set in their proper context.

Website: MESH Experience

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