How can you identify gaps in your market research team's forecasting skills?

Forecasting relies on historical data, making the assessment of past performance crucial. It is essential to examine inaccuracies and determine whether they stem from data issues, methodology, or external factors. A monthly review of these data points is recommended for robust forecast management. During the analysis, the team should be well-prepared with facts and solutions to establish an effective mechanism. Fostering a culture of constructive feedback is imperative to encourage continuous improvement, alongside prioritizing training, development, and cross-functional collaboration.

Demand planning for any organisation comes through quantitative data analysis as a process! Keeping ourselves aligned with market standards, past sales data and teams accuracy standards! Ensure industries best alignment practises is inline with teams practises! We should know the market acceptance of product, ensuring correct product positioning and tracking the required parameters around them! Periodically asses and validate the data against market standards, frequent benchmarking and ensuring validated data is to the fullest accuracy!

Also, having a benchmark helps in the majority of cases, if not in every. It helps in identifying and flagging unusual patterns, and further exploring these discrepancies to avoid them in the near future.

The purpose of gap analysis is simple — it helps your business improve upon its current state. By conducting gap analysis, you can improve your product, enhance process efficiency, and increase profits by identifying the various “gaps” present in your company.

Forecasting is a specialized skill, and market research isn't one-size-fits-all. Those adept at quantitative analysis may not excel in qualitative, and vice versa. Having said that, learning forecasting or any other quantitative research skills isn't a constraint. To assess team capabilities, begin by exploring the "how" – inquire about their approach to forecasting a specific assessment. Creative ideas distinguish proficiency in methodology. Evaluate tool knowledge and comfort with data. Consistent training allows learning through practical application. Engaging with numbers enhances adeptness in applying the methodology.

Delving into the essence of forecasting challenges, it's vital for any leader to pinpoint root causes. Drawing from my entrepreneurial journey, where scaling startups to INR 50 crores revenue in less than 5 years was a testament to strategic vision, I've found that gaps often stem from diverse factors. These may include data constraints, tool inadequacies, ambiguous goals, limited feedback channels, or even fluctuations in team motivation. Embracing tools like root cause analysis and fishbone diagrams has been instrumental in unraveling the complexities and steering my teams towards precision in forecasting. As we navigate these insights, it's not just about overcoming challenges; it's about fortifying the foundation for future successes.

Once you identify the gap in forecasting, it’s essential to understand the root cause. While there are several factors that contribute to forecasting gaps, three main factors stand out: 1) lack of data 2) unclear goals 3) insufficient skills or an outdated team. A lack of data can lead to misleading mathematical models and overly-optimistic forecasts. To address this issue, it’s important to collect and analyze data from multiple sources, including internal and external data. Regular skills updates and training are also essential to keep your team on track. Additionally, it’s important to ensure that your team has understood the need for the forecast. This will lead to a more robust forecast that will help reach your goals.

The main issues often stem from not having a clear focus on the topic and not aligning well with the study goals. Insufficient data is another problem, and this is where qualitative elements like expert interviews come in handy. Another challenge is having different ideas and data at different times, especially in complex studies, which can mess up the results. To avoid errors, it's crucial to stay well-organized and stick to the first principles thinking.

Review Past Forecasts: Conduct a retrospective analysis of past forecasts against actual market outcomes. This will reveal patterns of inaccuracies and the types of errors made.

After assessing forecasting skill gaps, the next step is identifying their root causes. Various factors may influence team performance, including insufficient data, tools, or resources, unclear goals or expectations, inadequate feedback or communication, and low confidence or motivation. Utilize tools like root cause analysis, fishbone diagrams, or the 5 whys technique to delve into underlying issues. Uncovering the reasons behind forecasting challenges helps address and rectify the root causes effectively.

Regular peer sessions and workshops might help in building the forecasting competency. Substantial time should be offered to the new joiners to understand the concept and not just the tool or process.

Let's make researches of market researches. Colleagues and competitors experience will help us understand how it goes: what data they used, what forecasts they made, and what came out in the end. Open your team to explore different forecasting cases in different fields.

Peer Review: Implement a peer review process for forecasts, where team members can critique and learn from each other’s work.

Having identified the root causes, develop a training and development plan to address forecasting skill gaps. Craft specific objectives, actions, and measures to help the team improve. Utilize various training methods like online courses, workshops, coaching, or mentoring based on team needs. Regularly provide feedback, recognition, and incentives to encourage application of learning and enhance forecasting performance. This comprehensive plan ensures continuous improvement and skill development within the team.

One strategy that has worked well in the past with my team is to turn some past forecasts into a case study. Put aside 1-2 hours with your team and ask them to bring up projects that they are most proud of. As a team, we go through the forecast results, benchmark them, and solicit each other's input on how to improve the results. I find this process much more engaging, and consider this as an interactive training and also team bonding activity. The benefit of this approach is that we encourage our team to consistently communicate and share their learnings with each other. Additionally, as a facilitator of this session, you don't really need too much upfront time investment in generating content.

Unfortunately, I have seen Healthcare market researchers forced to work on FinTech surveys and FinTech market researchers work on Healthcare studies. Mainly because staff were overworked or currently too busy with other projects. The amount of extra time needed to work on questionnaires, interact with the client and fix everything is significant and I do not recommend this approach. This leads to a poor project, unhappy client but also a huge error in forecasting for your own company. This is just one example, but I found it fairly common in larger market research firms I worked at. I advise you always keep industry/sector experts within their areas of expertise – don’t try to fool your client as they will easily see it.

In the final step, monitor and evaluate the results of your training and development plan for forecasting skills. Track progress and outcomes using the same metrics and methods used for initial assessment. Collect feedback from the team and stakeholders to gauge satisfaction and impact. Regularly review and adjust the plan based on results and feedback to ensure the team is meeting forecasting goals and effectively closing skill gaps. This iterative approach ensures ongoing improvement and alignment with objectives.

To identify gaps in forecasting there are two crucial things to keep in mind: 1. Any forecasting should be numbers driven. Numbers don’t lie. 2. The historical delta of the previous forecasting should be kept into consideration for next forecasts!

Finally, monitor and evaluate the results of your training strategy. Using the same KPIs as before, solicit input from your team and stakeholders. Review and update the plan based on results to ensure your team meets forecasting goals and effectively closes skill gaps.

Monitor team results by establishing clear performance metrics, setting realistic goals, and implementing regular progress reviews. Utilize project management tools, track key performance indicators, and encourage open communication to ensure everyone is aligned with objectives. Regularly assess outcomes against expectations and adjust strategies as needed.

By systematically evaluating both the outcomes of forecasts and the processes used to create them, you can identify where your team may need additional training, better tools, or changes in methodology to improve their forecasting skills. Addressing these gaps is crucial for maintaining the accuracy and reliability of market research forecasts.

It is important to check and review source of data . Sometimes sample size may not be fully represetnative. Secondly , normalisation for outlier / one off events in forecasting models is a common error .

An example I’ve seen is when our team relied their data solely on past trends and data. The problem with this methodology is increased fluctuation in external factors change outcomes differen than past interacting with eachother. Rather than reading data and creating statistics, teams should try to read root cause and behaviors leading to data change. This way forecasts include statistical projection coupled with logical predictions on behavioral pattern resulting with smaller deviations. As the world gets more volatile, past data will only mirror a portion of forecast. Missing pieces can only be completed with behavioral patterns.

Sometimes forecasting teams look too much inward and forget to do benchmarking with companies that face similar challenges and that have similar demand. Talking to other forecasters will certainly bring insights on how to collect, cross and interpret consumer and market data that will enrich the forecasting models in creative ways. Further, forecasting is a mix of hard data about consumer demand and ambition from the leadership team, who set targets that are aspirational yet believable.

Brainstorming to consider different approaches to forecasting helps in validating the results and evolving the forecasting process. Because many times, we just get caught in the loop of stringent timelines and opt for a generalized process with little or no customisation that might compromise the quality of the results.