We are continually developing our algorithms to help our customers build and maintain their business relationships. In June 2014 we reached a new milestone and are pleased to present the Implisense Smart Properties.
Implisense Smart Properties help you to segment existing and potential new customers more precisely. Up to now, it has been customary to use features such as size, industry sector and region to segment B2B customers. However, for many questions, these characteristics are not sufficiently detailed and lead to scattering losses.
Here is a practical example:
A marketing director for business training courses and seminars wants to find leads for the new seminar offer in the area of quality management. The question is how to select from many potential companies of a certain size and industry each one that has an affinity to the topic of further education and quality management. With the help of classical searches in databases, these characteristics are usually untraceable or obsolete. Searching the websites is not possible due to lack of time.
In the Implisense Sales Intelligence, the Marketing Director can now receive suggestions for leads that are similar to those of existing customers. This helps to get hits from neighbouring industries, for example. With the help of the Implisense Smart Properties it is now possible to display a so-called radar chart for each proposed company, which shows how strongly the respective company focuses on quality, costs or innovation. These companies tell the public that they rely on quality and learning in their products and services. The Marketing Director selects these leads to inspire the marketing manager with enthusiasm for the new offer.
And to make it even easier for you to address them, Sales Intelligence automatically calculates the average radar chart of companies from the rest of the hit list and displays it with a gray background underneath the blue colored current radar chart. This allows you to see immediately which particularities the selected company has in relation to the peer group, i. e. whether it emphasizes certain aspects more strongly than the other providers. This is illustrated in the following figure from the Sales Intelligence application. In this case, the selected company differs greatly from the rest of the hit list in the area of learning and reliability.
How is such a thing created? The Implisense Smart Properties are generated from online content using Big Data Analytics. For this purpose, previously trained topic models (Wikipedia link) are used for all target properties, which are automatically maintained by the Implisense system and thus prevent new words for certain topics from not being recognized. Topic-models (Wikipedia-Link) are used for all target properties, which are automatically maintained by the Implisense system and thus prevent new words for certain topics from not being recognized. Topic models can now be used to calculate mathematical distances to selected online content of a company and use them for new segmentation and insight into the customer base.