Getting more from your digital marketing investment

Harvard Business Review article entitled Closing the Gap Between Digital Marketing Spending and Performance looks at how businesses can optimize their marketing returns.

“Marketers know that digital marketing represents the future of their business. That’s why, according to the February 2022 edition of The CMO Survey, they’re happy to allocate 57% of their budgets to digital marketing activities and are planning to increase spending by another 16% in 2023. However, the survey also found that this contribution has weakened over the past year. More than 30% of marketers who participated said that they are experiencing average-to-no returns on their investments, which could create funding difficulties in the future if they are not able to overcome this gap.”

The article suggests several strategies for closing the marketing performance gap including the following approaches that could all incorporate predictive weather-driven demand analytics.

Doubling down on strategic experimentation: “We recommend companies increase these investments with an eye toward more strategic-level experimentation that can offer opportunities for breakthrough growth. Too often marketers get bogged down in tactical experiments, such as whether customers like green or yellow, instead of testing the relevance of new offerings, innovations, or customer segments.”

Planalytics has worked with leading retailers on A/B testing in digital campaigns to understand return-on-advertising-spend (ROAS) improvements that can be captured by marketing to audiences with positive weather-driven demand forecasts for specific products. It is not unusual to see a 2x-3x improvement versus the control group that has not been selected based on expected weather-influenced purchasing impacts.

Embracing a culture of innovation: “Marketing leaders can further digital transformation by helping build several organizational characteristics: a culture of rapid learning, strategic partnerships, specialist skills, and agile structures… What does this mean in practice? Companies that align their C-suite leaders across the business and focus on shared goals are better positioned for digital transformation. More specifically, organizations execute three key priorities to realize digital marketing transformation. First, they establish a common set of KPIs that ideally are aligned to business objectives, such as revenue, profit, or sales. Second, these organizations prioritize the customer first. And third, marketers truly understand how their customer makes decisions, and they upskill and reskill their teams to ensure that they can accomplish ever-more complex work.”

The impact of weather on sales IS KNOWN. Planalytics helps retailers precisely measure these impacts and proactively leverage the information with localized, product-specific metrics that clearly identify when and where spending can be optimized. Innovating with a metric derived from such a critical, everyday driver of consumer purchasing decisions and targeting in-market audiences yields significant gains on KPIs such as click-through rates, sales, and ROAS.

Investing in AI and machine learning: “Respondents predict that AI/ML use will triple to 38% over the next three years… We think this use and investment level should increase if companies are going to make the most of their data analytics investments… Marketers who integrate their first-party data with ML-driven marketing tech can optimize interactions with their most valued customers…”.

Planalytics utilizes a combination of proprietary statistical and ML techniques to create ready-to-use, highly tuned, feature-engineered analytics that are a natural fit for a retailer’s AI/ML environment. Planalytics weather-driven demand (WDD) metrics encapsulate more information than just raw weather, encoding large amounts of proprietary knowledge relating to consumer demand and weather based on decades of complex, real-world learnings. In our work with a national apparel retailer, we saw a huge difference in using WDDs instead of raw weather data in their ML model. This approach increased the model’s attribution of sales volatility to weather by 70%, the highest attribution of sales to weather the model has ever captured.  As businesses invest more in AI/ML using tested, proven, feature-engineered inputs like WDD can accelerate optimizations and the associated financial returns.

Learn more by visiting