Sales

Product sales are impacted by many factors, both controllable and uncontrollable. Predictive models help retailers disentangle the real factors from spurious factors and position them to make informed decisions around factors within their control. Calligo considers sales data, customer data, store data, local and macroeconomic data, regulatory data, weather, and seasonality to build powerful machine learning models that determine the real drivers of sales so that you can achieve maximum profitability.

Anticipated demand for new products is largely based on the performance of similar, previously sold products. This product sales history points to consumer preferences, which includes insights for limited product release or product testing. With a better understanding of demand, tech companies can choose how much of a product to produce to meet customer demand, limiting losses from unsold inventory. In addition, anticipating demand for a new product can lead to more strategic decisions around product development efforts. Calligo’s solutions use a combination of market research, customer history, product attributes, and sales history. These factors, in combination with machine learning models, accurately predict product demand.

Assigning the optimal price for a product maximizes profit. It positions businesses to capture either higher sales or higher margins per unit of sale. Optimizing product prices is central for any business, leading to higher profitability. Maximizing gross margins per unit requires high prices. However, maximizing sales requires low prices. At Calligo, we help businesses optimize product pricing and maximize profit by combining cost data, customer data, and input costs in our models. For online customers, we leverage A/B tests to determine the optimal pricing of a product. Further, our models determine optimal pricing for each customer based on their unique characteristics and behavior.

Understanding sales trends over time can lead to better decisions around inventory, products, expansion, or store closures, all of which increase revenue or reduce costs. Predicting sales trends over time is difficult because sales are affected by consumer preferences, seasonality, and the macro-economic environment among other things. By understanding the impact of these features, we can leverage time-series analysis and machine learning models to identify your current underlying trend, project where it will go, and give you the information needed to make the changes that will drive success. Calligo’s models combine your sales and market data with competitor data to show you what impacts your business most. Further, our models simulate changes and allow you to forecast sales based on changes you consider making.

Effective customer segmentation through machine learning techniques such as clustering and collaborative filtering enables targeted marketing, profitable product selection, and informed expansion strategies by uncovering meaningful customer groups and hidden customer-product relationships with Calligo’s powerful solutions that utilize customer data, external data, and domain expertise.

Calligo’s machine learning churn prediction models analyze historical customer data, identifying patterns and signals that indicate potential churn. By accurately predicting which customers are at risk of leaving, sales leaders can take proactive actions to retain these valuable clients. With this foresight, sales teams can implement targeted retention strategies, such as personalized outreach, special offers, or loyalty programs, to address the specific concerns of at-risk customers. By reducing churn, businesses can maintain a loyal customer base, minimize revenue loss, and enhance their reputation for excellent customer service.

Leveraging advanced lead scoring models, Calligo’s machine learning enables sales leaders to prioritize leads based on their likelihood to convert. By analyzing data from various sources, such as website interactions, social media engagement, and past purchase history, the system assigns a score to each lead, reflecting their potential as a qualified prospect. Armed with this insight, sales teams can focus their efforts on high-scoring leads, increasing efficiency and productivity. As a result, sales representatives can concentrate on building relationships with the most promising leads, leading to improved conversion rates and higher revenue generation.

Calligo’s machine learning algorithms analyze customer behavior, preferences, and historical purchase patterns to identify cross-sell and upsell opportunities. By understanding the complementary products or higher-tier offerings that customers are likely to be interested in, sales leaders can tailor their sales strategies accordingly. This allows sales teams to make relevant product recommendations to customers, increasing the chances of additional purchases. By effectively capitalizing on cross-sell and upsell opportunities, businesses can boost average order value and increase revenue without significantly increasing customer acquisition costs.

Calligo’s machine learning-powered sales performance analysis goes beyond basic metrics, delving into nuanced data to provide sales leaders with valuable insights. The system can assess individual sales representative performance, track sales team trends, and identify areas for improvement. By understanding the strengths and weaknesses of their sales teams, leaders can implement targeted training and coaching programs to enhance their skills. Additionally, the analysis can reveal patterns in successful sales strategies, enabling sales leaders to replicate and scale those approaches across the team. With data-driven decision-making, sales leaders can optimize their team’s performance, improve efficiency, and drive revenue growth.

Working with you to identify the specific ML use cases for your business with clearly defined outcomes, using our Data Science by Design methodology.

Identifying the most beneficial use cases for your business needs and applying deep industry experience to de-risk the solution. Resulting in a higher return on your investment.

Unlike others in the market, we combine innovative technologies and human intelligence to deliver solutions that are 25-50% cheaper than off-the-shelf alternatives.

By leveraging the specialist expertise and hands-on experience of our data scientists, we execute highly effective and impactful ML strategies at pace.

85% of ML projects fail to deliver, with only 53% reaching the production phase. At Calligo, our process optimises the rate of ML solutions moving from prototype to production.

We pinpoint our data science skills against your unique objectives, resulting in highly accurate, targeted outcomes, compared to out-of-the-box solutions.