Il Churn Rate nelle aziende no profit
Success case delle aziende no profit e come hanno utilizzato Bigprofiles
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Overspending identified in national channels, such as linear broadcasting and radio
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Overspending identified in national channels, such as linear broadcasting and radio
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Potential savings through reduction or reallocation to more efficient state channels — with improved inbound marketing results
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In linear broadcast ad spending better attributed and allocated
Challenge
Determining the ideal usage of an extensive omnichannel marketing budget
With a marketing budget exceeding $1B, this company could pursue any and all marketing efforts it desired. But which were worth the investment? That determination was especially difficult for the roughly 30% of the budget allocated for linear TV and radio advertising, where obtaining reliable data is challenging. The company also had a relatively small amount of digital marketing data from ad platforms.
Additionally, the existing impression, click, and attribution data didn’t provide sufficient analytical details to inform planning, especially since typical attribution methods reveal only what happened in the past. With business performance faltering, the marketing analytics team needed to boost marketing efficiency and make the best possible use of their budget.
"Il Churn è il problema più grande per le no-profit"
"Churn rate predictions"
Solution
Understanding and optimizing marketing spend
The marketing analytics team was most interested in understanding and increasing inbound inquiries from potential customers. Therefore, Pecan’s experts collaborated with the team on constructing a marketing mix modeling (MMM) solution to predict the number of inquiries based on ad spending.
Simulating the impact of potential budget changes with MMM
In addition to predictions, a key feature of MMM is the power to simulate different marketing budget allocations and see likely outcomes. With a click, marketers can examine the potential results of increasing or decreasing state- and national-level spending, as well as the effects of moving spend among channels.
Results
Understanding and optimizing marketing spend
With the MMM model in hand, the marketing team has far more insight into how spending in different channels drove customer inquiries. MMM shows each channel’s impact, including channels that don’t provide reliable data on audience responses. That includes the linear broadcast ads that were a key channel for this company. Additionally, the team could use Pecan’s simulation and optimization tools to test different approaches’ impact.
While the MMM solution is already proving its worth, it could have an even greater impact in the future. Pecan’s experts are exploring options for refining the model, which may include not just predicting customer inquiries, but also optimizing marketing spend based on both customers’ predicted lifetime value (pLTV) and cost per acquisition (CPA).
"-30% abbandoni""
