Crystal Ball Image Source GriloPecan AI has published research based on a survey conducted by Wakefield Research entitled “The State of Predictive Analytics in Marketing 2022”. The key and disturbing finding from the report is that 84% of marketing executives agree that it is still difficult to make day-to-day data-driven decisions and take action. 44% of respondents see their ability to predict customer behaviour is often guesswork, and 8% admit it is always guesswork. One worries that 8% of marketers are being brutally honest. What is the real percentage?

These percentages are even more concerning when one considers the other statistics from the survey answers:

  • 95% of companies now integrate AI-powered predictive analytics into their marketing strategy
  • 44% indicated that they’d integrated AI-powered predictive analytics into their strategy completely

The findings are based on a survey of 250 senior marketing executives with the title of director and above at B2C companies that use predictive analytics with a minimum annual revenue of $100 million.

The report, at twelve pages in length, has an executive summary and is then divided into five sections. Each section highlights and explores a key finding from the survey.

  • AI Ambitions and Obstacles Are Universal in Marketing
  • Marketing and Data Science Alignment is Elusive
  • Despite Data Efforts, Many Marketers Feel “Guesswork” Guides Decisions
  • Goodbye Actionable Insights – Marketers Crave Impactful Predictions Specific to KPIs
  • Prove Marketing ROI Efficiently With New AI Approaches

Each section provides a mix of analysis and data findings. At the end of each section the authors have added two questions that prompt marketers to consider their approach to predictive analytics for marketing. These ten questions provide a valuable take out for the reader. The report closes with further reports available from Pecan.AI.

The key findings

AI Ambitions and Obstacles Are Universal in Marketing

Highlights the usage of predictive analytics by marketers. Only 51% are using customer level predictions of future behaviour. This is despite the growing calls for personalisation. The report also looks at where marketers are likely to deploy capabilities in the future with 46% predicting churn and retention on a customer level being the highest. There are challenges though, with the high cost of manual data science and concerns over data at the top of the list (40%).

Marketing and Data Science Alignment is Elusive

This section delves into the challenges of deploying data science solutions, with marketers often fighting other teams for services. Whether this is a lack of availability of data scientists or a priority issue is unclear. Marketers are struggling to overcome the challenges, and there is no clear and consistent path to resolving the issue. Can Pecan.AI solve this challenge?

Despite Data Efforts, Many Marketers Feel “Guesswork” Guides Decisions

One of the key findings from the reports highlights that while predictive analytics is used across marketing, it is not trusted. This is partly because the changing economic climate and other disruptions mean the predictions rapidly become outdated as circumstances change. For many, it seems there is no way to connect predictions with actions. The inference is that prescriptive analytics is needed as predictive analytics is not accurate enough once the time factor is introduced.

Goodbye Actionable Insights – Marketers Crave Impactful Predictions Specific to KPIs

What do marketers want? The survey highlights predictions should align with business KPIs. 60% of marketing leaders said they wanted AI to “uncover specific KPIs from my data instead of scouring for potentially useful insights.” It is no longer enough to predict what customers will do. The suggested actions must ensure the realised outcomes are valuable to the business.

Prove Marketing ROI Efficiently With New AI Approaches

For any leaders looking to invest in a software solution, the CFO wants a clear understanding of the ROI it will produce. CFOs are no longer the number cruncher measuring historic ROI. They are becoming strategic advisors to the business, predicting financial outcomes from different strategies. Marketers need to evaluate and demonstrate the ROI. However, 83% find that somewhat-to-extremely challenging. Alarmingly 100% of respondents are facing cutbacks on investment, ranging from, a little (23%), Some (40%), a great deal (31%) to completely (6%). A better ROI analysis might help to change that.

Exposing the truth

Zohar Bronfman, Co-Founder and CEO of Pecan
Zohar Bronfman, Co-Founder and CEO of Pecan

Zohar Bronfman, Co-Founder and CEO of Pecan, commented, “With most companies today employing manual model building approaches, it’s unfortunate, but not surprising that the results are failing the needs of marketing teams. While data scientists may be skilled in building the perfect software models, they are simply too far removed from the nuanced realities of the business to be effective. In addition, given their workloads they are too slow to respond when considering the rapidly changing market conditions and consumer behavior. Marketers and marketing analysts are more than capable of handling predictive analytics responsibilities if provided with the right tools.”

This failure of predictive analytics in Marketing is something that Pecan AI believes it can address. Its low code platform enables organisations to drive insights from marketing data directly into business functions where it matters. Use cases include conversion rate modelling, customer churn, demand forecasting, upsell & cross-sell information. Hydrant, a US-based wellness brand, increased conversions of win-back offers by 260% and revenue per customer by 310%. John Sherwin, CEO of Hydrant, commented, “Pecan’s predictions informed our marketing efforts, helping us reach out to the right customers and allocate spend in the right places. The models were incredibly accurate in identifying which customers would more likely respond to our offers and make new purchases.”

Enterprise Times: What does this mean?

This report is potentially a risky undertaking. What isn’t clear is how Wakefield sourced the respondents. Are any of the survey respondents customers of Pecan.AI? If they are, the results could be concerning for the company as it could infer they are not meeting their expectations on delivery. That all respondents are using predictive analytics in marketing raises a question as to whether Pecan withheld the information about what solutions those firms are using. If they did, then expect Pecan to leverage that information as it looks to replace other solutions.

In summary, this is a solid report with interesting data and thought-provoking questions that any marketer should consider. The gut and data-driven decision-making argument should no longer be an issue. However, unless marketers can trust the data and algorithms that AI uses to surface insights, it seems that the old ways persist for many.


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