Only 17% of B2C Marketers Feel Their Data-Driven Strategy Help Achieve Their Goals

by SpiceWorks

Data analysis and utilization are necessary for a B2C company’s growth. But how can marketers from these companies successfully predict future business trends and customer behaviors to have a competitive advantage? Pecan AI and Ascend2 recently conducted a study to get some answers. Check out these insights from the study.

Analyzing and utilizing data is critical for a business’s growth for most consumer-driven companies in today’s world. But how can business-to-consumer (B2C) companies gain a competitive advantage by predicting future customer behavior and business trends? To answer this question, Pecan AI and Ascend2 surveyed over 200 marketing professionals working in companies operating in the B2C channel. The following are the insights from the study.

Few B2C Companies Consider Their Marketing Strategy Successful

According to the study, only 17% of B2C marketers felt their data-driven marketing strategy was very successful or best-in-class in helping them achieve their goals. About 72% believed they had some success from their data-driven strategies. This indicates that there is still room for improvement regarding utilizing data to make strategic decisions in marketing.

Data-driven Marketing Is Most Useful in CX and Customer Journey Mapping

There are certain areas where data-driven marketing is most useful. According to 51% of respondents, it was most useful in customer experience (CX) or customer journey mapping. According to 43%, personalization was the most useful application, and for 39%, it was email marketing.

Areas in which data-driven marketing currently most useful

Areas in which data-driven marketing is currently most useful 

Source: Using Data to Predict Future Performance

Many Marketers Expect Budget Increase for Data-driven Marketing

About 44% of the study respondents expected increased budgets for data-driven marketing efforts in the coming year. But only 6% said that this increase was significant. About 46% said their budgets would remain unchanged, while 10% said the budget for data-driven marketing would decrease in the coming year.

Certain Data-driven Trends Will Be Most Critical To Decision-making Process

According to 61% of respondents, improving CX would significantly influence the B2C decision-making process in the year ahead. For 51% of respondents, increased personalization will significantly influence the decision-making process. Interestingly, only 13% of marketers felt that increasing first-party data collection and use would be critical to their decisions in the year ahead.

Data-driven marketing trends that will be most critical to the decision-making process

Data-driven marketing trends that will be most critical to the decision-making process

Source: Using Data to Predict Future Performance

Very Few Marketers’ Data-driven Strategy Is Prepared for Loss of Third-party Cookies

Only 9% of respondents strongly agreed that they had prepared their data-driven strategy for the potential impact caused by the loss of third-party cookies. About 44% somewhat agreed that their data-driven strategy was ready. About 47% somewhat or strongly disagreed that their data-driven strategy was ready for the potential impact. 

B2C Marketers Want the Ability To Predict Certain Things

According to the study, 39% of marketers would like to be able to predict customer lifetime value (LTV), and 34% would like to have the ability to predict sales close rates. About 33% would like to be able to predict customer upsell and cross-sell opportunities, while 29% would like to be able to predict budget allocation and spending.

About 95% of marketers agreed that being able to predict the impact of changing circumstances on their business would help them improve their overall data-driven strategy. Gaining foresight into how changing circumstances could possibly impact their organization’s bottom line provided greater agility and preparedness for a marketing strategy.

Few Marketers Leverage Insights From Predictive Analytics and Machine Learning

Only 8% of the survey’s respondents reported leveraging the insights from predictive analytics (PA) coupled with machine learning (ML) in their data-driven measurement approach. About 27% planned to use intelligence tools with PA in the year ahead. Further, 40% would like to use the insights but have no plans in place. The tiny percentage of marketers using insights from PA and ML is a huge missed opportunity to optimize predictive insights using artificial intelligence’s (AI) power. 

Marketers Face Challenges in Extracting Accurate Predictive Insights

According to 35% of B2C marketers, the greatest challenge they face in extracting accurate predictive insights from their data is implementing a strategy to get started. For about 15%, data cleaning and engineering requirements are a major challenge. For 12%, inadequate data science resources are a key challenge. Over 27% of the surveyed people did not yet use predictive analytics to improve performance.

The biggest obstacle in extracting accurate predictive insights from data

The biggest obstacle in extracting accurate predictive insights from data

Source: Using Data to Predict Future Performance

Key Takeaways and Future Steps

There are three key takeaways from the study and steps B2C marketers can take based on these insights.

1. There is a stronger want among marketers to get closer to the customer, either driven by the economy or the desire to create lifelong customers. Marketers indicated that mapping customer journeys and predicting LTV, upsell/cross-sell as key applications when considering data-driven marketing opportunities.

Action step: AI now makes it possible to predict customer LTV at the beginning of a new relationship. Hence, use the technology to determine your most profitable customers at the beginning of their journey.

2. Few marketers felt prepared for the ongoing privacy changes and developing first-party solutions. This is reinforced by the idea that many marketers felt the hardest part of implementing a data-driven marketing framework driven by AI and ML is not having a strategy. Several third-party tools help brands get started with their data-driven roadmap.

Action step: PA helps you see what is next for your business. That said, there are several potential strategies you can consider and implement. Hence, determine the best course of action for implementing PA in your business.

3. Most marketers agreed that being able to predict shifts in the customer landscape would positively impact their business. Yet, only 8% use predictive analytics today. Given the growing uncertainties in today’s world, brands should consider incorporating AI and ML in their analytics frameworks.

Action step: Marketers can consider intuitive platforms that help them add data, deploy off-the-shelf predictive models, and see outputs. 

By taking the steps mentioned above, marketers can create a strong data-driven approach to predict future trends and gain a competitive advantage.

Article originally appeared on SpiceWorks.

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