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Sales Intelligence Services

 

Powering B2B Sales
with Intelligence

We help customers infuse their sales, marketing, and operations with data-driven intelligence. Our agile team of passionate data scientists, data engineers, and software developers focuses precisely on your needs, efficiently tackling challenges with data integration, data enrichment, analytics, and machine learning to make your business smarter.

We harness your internal data and enrich it with external data, then use this amplified data asset to understand, model, and predict customer behavior – allowing your sales team to optimally target their efforts to drive desired outcomes.

We help you harness the new science of B2B sales

While the potential of the new science of B2B sales is astonishing, many companies struggle to implement it because of problems with data quality, lack of integration across data silos, and shortage of data scientists. 

Valuemotive’s core competence is in combining expertise in analytics - especially computational linguistics and natural language programming - and software development for efficient model development and testing. We provide an efficient workflow for the customer, reducing project costs, and greater chances of success, improving chances of wider organizational adoption.

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Services we offer

Customer Churn

Keeping existing accounts is crucial for growth. Predicting which accounts are in danger and what are the main reasons for them to leave opens up an opportunity to act. We can build monitoring of the purchase behavior and its changes. This information can be used to detect the possible usage of competitive providers and trends in the customer’s business and needs.

Finding new prospects

Using existing customer data to find success factors and then creating new prospects is a powerful way to make meaningful connections. Valuemotive’s B2B SI can create accurate reports taking into account for example revenue models and characteristics of the customer base.

Upselling

Rapid detection of purchase intentions, which can be uplifted for a higher value. We can consider customer profiles when recommending alternative solutions for them. In previous projects we’ve been matching purchases against alternative sets and offerings. We’ve generated new recommendations for compatible alternatives for a better price, availability or value.

Regular Purchases

Machines wear out expectedly depending on the usage and conditions. Some customers buy new items in random intervals as the life cycle comes to its end while others buy larger quantities at once in order to avoid outages and to get higher discounts. Modelling suitable timelines based on customer behaviour can create for example a two months ahead reminder for contact. This enables a customer connection before they’ve proceeded with the competition.

Cross Sales

Online stores often have thousands of kinds of different products and it has hundreds of regular customer companies. Some of the sales are based on direct sales and quotations but most of the transactions are coming from the on-line shop. Customers tend to cherry-pick items from their catalogue and purchase cheaper items from other providers.

By understanding which items are often purchased or browsed together it is possible to recommend additional items for the customers before they close the deal. The on-line shop applies dynamic discounts for the recommendations based on the profile of the customer company. The discounts are calculated with a model that tests how sensitive customers are for the price of these missing parts.

Learn from the Feedback

Customer feedback, especially text-based open answers, is read a lot by human effort. Due to the amount of work involved, the utilization of the material can be improved by automating its processing. Factors influencing sales and business, such as customer needs and desires, are searched mechanically from the materials. Emphasis is placed on findings that can be acted on.

We can process customer feedback and satisfaction questionnaires for frequent requests or observations and continually report these, taking into account different geographical areas and other segments. The effectiveness of the acts can be observed as in decline or increase in their trends.

We can convert customer feedback and discussions to the form that is suitable for other customer analytics. We’ll extract and score the relevant topics for each customer so that they can be included in the loyalty models, segmentation, priority scores, etc. You can still use your favorite tools for analytics and reporting with the new information telling more about the actual needs and thoughts.

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Projects

Sales intelligence infrastructure & data development

We work hands-on with a multinational corporation as they transform their sales process. To create a solid basis for analytics & AI, we work on maintaining and developing the client’s cloud infrastructure and data warehouse, clean and prepare data and then use our proprietary tools to enrich the client’s internal data with relevant external data.

Predictive intelligence & sales automations

We have been helping a multinational client optimize their sales by building predictive intelligence and automation into their sales process. We are helping the client predict customer needs and motives, predict deal closure probabilities, analyze customer purchasing behavior to drive customer-specific sales strategies, provide contextual pricing, and automate up-sell and cross-sell.

Customer purchasing behaviour modelling & segmentation

Working with a medium-sized client, we combined internal and external data to forecast customer lifetime value and model customer purchasing behavior. This created deeper understanding of customer behavior, making it possible to segment customers in new ways based on their value and purchasing behavior patterns. As a result, our client could adjust their sales strategies optimally for each customer segment, targeting marketing and sales efforts to customers close to identified purchase trigger points.

Creating data-driven sales communications

Working with a medium-sized client offering a digital marketplace to its B2B customers, we created a new data product for them to boost their sales by being able to numerically demonstrate the value of their marketplace to customers. We utilized a unique data set built by the client, enriched it with external data, and modeled it to generate insights into marketplace dynamics. We highlighted insights into pricing and sales cycles, which furnished our client with the evidence of the value its creates for its customers by enabling them to receive higher-than-average prices and close sales in shorter-than-average cycles. Having this evidence at their fingertips gave our client’s sales team a great tool to power their negotiations with potential customers and helped them boost sales