What actionable insights can your data tell you about your customers?

By combining historical data from your ERP, CRM and POS systems, we can identify patters for customer behavior and adjust metrics that will hint on traits like openness to new things. For example, wealth management teams in banks often need to understand the degree of a customer's risk aversion. Self-reporting of clients might be useful - but not always reliable. Integrating all internally available information about a customer and combining them with external data like social networks, can help draw a more accurate picture about a customer's risk taking behavior. This will better inform investment strategies a customer can identify with. Knowing about a customer's openness correlates, for example, with political orientation (learn more), which can inform a political parties marketing efforts. Adding risk taking behavior and buying power into the mix can help a travel agency with their targeted marketing by suggesting travel destinations. Learn more about how we can derive insights about your customers in your industry by getting in touch.

How will the demand change in the next 6 months?

The demand for specific products is often partially dictated by seasonality (e.g. most diamonds are sold by the end of the year). Besides seasonality, there can be upwards/downward trends, which are often visible in the data. Our first step to tackling demand forecasting, is to separate seasonality from the trend. We then build a model that reflects your internal data, and complement it with external data. For example, while the number of school holidays in a time window has strong impact on the seasonality, the trend will largely be unaffected by it. However, a drastically lower price for energy storage would very quickly lead to a strong trend in electric cars. We can help you quantify either, and use them to forecast future demand. As with any forecast in time-series, the further the prediction, the less accurate it is. Our models output so-called 95% confidence-intervals with their predictions: the minimal interval containing the actual demand with 95% probability.

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What actionable insights can your data tell you about your customers?

By combining historical data from your ERP, CRM and POS systems, we can identify patters for customer behavior and adjust metrics that will hint on traits like openness to new things. For example, wealth management teams in banks often need to understand the degree of a customer's risk aversion. Self-reporting of clients might be useful - but not always reliable. Integrating all internally available information about a customer and combining them with external data like social networks, can help draw a more accurate picture about a customer's risk taking behavior. This will better inform investment strategies a customer can identify with. Knowing about a customer's openness correlates, for example, with political orientation (learn more), which can inform a political parties marketing efforts. Adding risk taking behavior and buying power into the mix can help a travel agency with their targeted marketing by suggesting travel destinations. Learn more about how we can derive insights about your customers in your industry by getting in touch.

trending_up

How will the demand change in the next 6 months?

The demand for specific products is often partially dictated by seasonality (e.g. most diamonds are sold by the end of the year). Besides seasonality, there can be upwards/downward trends, which are often visible in the data. Our first step to tackling demand forecasting, is to separate seasonality from the trend. We then build a model that reflects your internal data, and complement it with external data. For example, while the number of school holidays in a time window has strong impact on the seasonality, the trend will largely be unaffected by it. However, a drastically lower price for energy storage would very quickly lead to a strong trend in electric cars. We can help you quantify either, and use them to forecast future demand. As with any forecast in time-series, the further the prediction, the less accurate it is. Our models output so-called 95% confidence-intervals with their predictions: the minimal interval containing the actual demand with 95% probability.