Agribusiness marketing teams could attract better ad responses with predictive intelligence insight

Intelligence is best described as the ability to accumulate knowledge, to understand it and then apply it for answering questions and solving problems. In agribusinesses, market intelligence is typically something that teams learn through experience of their product, the market, previously tested advertising creative and channels, as well as price point. Teams will overlay market research into their specific channel for further insight to then solve a problem: attracting farmers to buy their product. 

By default, marketing planners rely on insight data from the past, accumulated and tested for what’s worked and what hasn’t, and applied to future scenarios. By testing and compiling results, retrospective information can analyze success – but what if you could apply this to future scenarios? 

When intelligence is used for understanding likely future questions, problems, and situations, and to find answers and solutions to the same, we refer to it as predictive intelligence . This is a continuous process, and means we get to expand and recalibrate our knowledge and navigate manifold situations.

Supporting more robust marketing strategy

A robust marketing strategy that influences where it can the key pillars of the wider business plan. Marketing strategy typically spans 3 years; leading with a brand story that relates in detail to a brands’ positioning against competitors, unique benefits as well as tangible reasons for a potential customer to believe your brand story. 

But how do you align your appeal to customers – through brand and advertising – to a niche and dynamic audience such as farmers? Predictive intelligence can be remarkably useful thanks to the macro and micro market data and extensive modelling, that allows marketing teams to get ahead of the myriad of issues that can affect growers from season to season. From cropping issues and efficacy to pest and disease challenge, all of these insights can be used to create the golden nugget that forms the basis of campaign content or creative focus. 

For the planning team, predictive intelligence can support ROI prediction by anticipating their share of market based on competitor activity and product awareness among others. Planners will also be in their element as they can zoom in to a territory plan to key grower personas, their customer journey and demographic behaviors. 

Ultimately, planners and strategists are able to allocate market funding and work with media planning teams based on smart recommendation and ‘what if’ scenarios in their respective territories. 

As many strategists will attest, measurement is essential to management and attributing the effectiveness of a campaign is necessary; but where print media is used there is little direct sales data, with awareness about the only tangible measure available. Digital media fares better in terms of action, however even then there is often a disconnect between marketing and sales as CP products are not available for direct sale. With Kynetec’s predictive intelligence, the tracking toolset allows users to align marketing and sales with field level information and combine it with retail transaction data. 

Sales teams get the jump with optimal pricing strategies

The importance of price for driving demand is a key variable in agribusiness, particularly when CP products come off patent. Price analytics helps sales teams understand how sensitive customer demand is to changes in pricing. By identifying the optimal price point, sales teams can maximize revenue and market share, ensuring competitiveness in the market. Not only that, leveraging price elasticity enables pricing strategies, where prices can be adjusted based on market conditions, demand fluctuations, and competitor pricing. 

Organizing sales teams can also be adjusted using the Sales Territory Optimization tool, ensuring the best use of sales resources as well as allocating territory sales targets according to local market conditions. By using historical sales data with other relevant sources of dynamic ag data to forecast future sales trends, the predictive intelligence platform allows for refined sales forecasting. It also permits for targeted account management, helping to identify new areas of opportunity and plan focused campaigns to sell the right product to the right farmer at the right time using local market size estimates. 

Retaining customers is easier than attracting new ones

It’s easy to state that current customers are easier to retain than replace, but how do you identify those customers who are at risk of churn? Does your marketing strategy even identify ‘at risk of churn’ customers? 

Within predictive intelligence, Churn analytics allows users to identify patterns and factors leading to customer attrition. By understanding what is driving churn, planners can proactively implement retention strategies. Analyzing churn data also helps in optimizing customer lifetime value. By identifying high-value segments in a strategy, specific marketing and sales tactics can be deployed to maximize revenue and profitability over the long term. 

Churn analytics also allows for honest feedback around customer dissatisfaction and reasons for leaving. This priceless information can level-up the following years’ marketing plan, with reasons for not buying sometimes more insightful than reasons to buy.   

Want to up the performance of your sales and marketing strategy? Predictive Intelligence is available now. Find out more here >

Or email Nomman at nomman.ahmed@kynetec.com 

Nomman has over 10 years of experience in agronomic research. Holding a Ph.D. in Agricultural & Development Economics from Justus Liebig University in Giessen, Germany, he has a strong academic background. He has spent several years in academia, including at the Centre for International Development & Environmental Research in Germany. During this time, he coordinated a consortium of scientific research centers in Central Asia and taught courses on world agricultural markets. An author and researcher, Nomman has written a book on climate change adaptation in agriculture employing a structural equation model. He has contributed to agricultural research through numerous peer-reviewed publications and papers and presented at events covering agricultural and climate change economics, agchem, and seed markets. With a focus on practical expertise, Nomman's career has been characterized by a commitment to academic rigor and contributions to agricultural knowledge.