Performics POV: Elevating Retail Measurement

Currently, many retailers utilize a high-level direct response/ROI model (ad spend to sales) for retail measurement.  Performics has recognized an opportunity for these retailers to elevate their retail measurement approaches through strategies like (1) category-level optimization, (2) customer data integration and (3) multi-channel attribution.  This POV outlines the potential future state of retail measurement, including significant gains that can be achieved by evolving your retail measurement practice.

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Old Model

The old ROI measurement model currently takes into account merchandising priorities through planning, but it doesn’t consider merchandising inputs like product margin, overstock/under-stock or seasonality (end-of-season, liquidation, etc.)  In other words, most retailers’ merchandising priorities aren’t fully reflected in their paid search program—every sale is treated the same.


By evolving our measurement approach, we can bid up based on the true value of each sale, instead of treating every sale the same.  For example, we can push products that need to be cleared from inventory.  And we can integrate product margins into bid strategies to bid up keywords associated with high-margin products.

To do this, Performics closely aligns with our clients’ merchandising teams to understand priorities and strategic aspirations.  Category-level optimization requires setting up a culture and infrastructure to make merchandising data available for bid strategy, in real-time.  We can integrate this data into our platforms via a custom data feed.  This can also be done by setting campaign-level KPIs that are determined through an analysis of product values, but ideally, it would be automated via data integration from the retailer once the values are known:

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Old Model

Under the old retail ROI model, retailers are also not able to leverage customer CRM data for optimization.  The ROI model treats every customer the same.  In reality, new-to-file, current and loyalist customers have different values.  By considering only sales and ROI, retailers are currently leaving out customer value.


By integrating customer data into your measurement model, you can do things like bid up based on the true value of each customer, instead of treating every customer the same.  For instance, once we integrate back-end CRM data associated with certain products, categories or keywords, we can take into account customer life-time value (new vs. existing) to focus on acquisition vs. retention paid search strategies.  To elevate old retail measurement models, we develop processes to obtain our clients’ CRM data—in as close to real-time as possible—via a data feed; or, we set campaign-level KPIs through analysis of customer type.  We then integrate the feed into our reporting and analysis to power customer-value optimization.


Old Model

Currently, most retailers utilize last-click-based measurement models.  However, shoppers are moving in a complex, nonlinear spiral across brand-search keywords, generic-search keywords, content, social networks, television, in-store and more.  A last-click model lessens our ability to optimize bids and budgets across keywords and channels.  For example, if we want to push shoes, we’ll bid-up last-click search keywords that drive shoe sales.  However, there are other keywords (and channels) before the last click that assist in selling shoes.  The retailer is missing opportunities to invest in these keywords/channels because—under the old model—we can’t identify assists.


Retailers have an opportunity to shift away from last-click ROI by leveraging attribution.  How do keywords/channels/devices interact with each other?  Where (and when) is the right place to spend each marketing dollar?  Answering these questions requires an attribution framework, which comes in a few different flavors:

For search marketers, attribution revolves around the interaction between brand and generic keywords—as well as the interaction between paid and organic search—in producing  the end sale.  Few search marketers leverage attribution, giving those that do a major advantage.  Search marketers must move beyond “last click” attribution; they must consider the searcher’s entire click path.  By identifying and investing in high performing keywords with direct and assist value, search marketers can increase performance.  By identifying and pausing keywords with no direct or assist value, search marketers can decrease costs:


For digital marketers, attribution is focused on channel integration—search, social, affiliate, display.  To start, cross-channel attribution requires a strong data foundation to track data from multi-channel touchpoints.  We can then plot intersections between paid, owned and earned channels to exploit synergies by fluidly shifting budgets from channel-to-channel, in real-time, based on demand or profitability.  Additionally—once you understand how your participants move across the web—you’ll be better able to activate highly synergistic and relevant cross-channel campaigns.

For CMOs, attribution is about a constructing a singular view of the customer, online to offline.  For instance, we know that shoppers use mobile devices to help them find stores.  Attribution can be used to measure the impact of online ads on in-store sales, thus influencing budgeting and media mix models.  There’s a variety of flavors of online-offline attribution, including:

  • Panel Match:  Matching transaction data from your in-store credit card purchasers who match comScore’s online panel to identify the offline lift from paid search (brand and generic), organic search (brand and generic), display or social.  Using Panel Match for a multichannel retail client in 2012, Performics found that paid search drove an average of $11.71 in in-store sales for every dollar sold online.
  • Matched-Market Testing: Designing and executing experiments on a regional basis to measure offline lift caused by changes in online marketing activity
  • Modeling: Identifying correlations between online and offline sales by leveraging data and models.  A common application is modeling the effect that your TV ads have on search volume.  This informs paid search budget flighting strategies to capture demand you’ve created via TV, in real-time.

To build an attribution framework, we recommend: 1. Identifying all currently available data streams to understand attribution opportunities that are currently available to you, without additional work.  Sources of digital attribution analysis include:

  • Platform-Based Attribution: Leveraging the search engines or search management platforms/ad servers (e.g. Marin, Atlas) to supply attribution infrastructure
  • Clickstream/Channelstream: Performics’ proprietary attribution models that leverage search management platform log files
  • SinglePoint Platform Partnerships: Third-party attribution platforms like ClearSaleing, Adometry and VisualIQ

2. Identifying the ideal state of attribution for your brand, including:

  • Cross-Digital Attribution: Complete a tagging audit to reveal if you have all the tagging capabilities in place to understand all participant interaction points across the decision journey, not just the “last click” before the sale.  This audit may reveal the need for additional tags or technology.
  • Onlineto-Offline: Measure how online behavior affects offline behavior (and vice versa) by utilizing methods like Panel Match
  • TV-to-Search:  Access media calendars to flight search campaigns alongside TV in order to measure the impact that TV has on search impression volume and clicks

Elevating retail measurement through category-level optimization, customer data integration and attribution will help you better allocate media weight between keywords and channels to maximize performance at the lowest allowable cost.  Contact Performics today to further explore these retail measurement strategies.

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