B2B is quickly transforming and adapting to better and more sophisticated ways of analysis and marketing campaigning. Technology advances have only helped in this endeavour. In such a favourable environment, how can you make the most of advanced analytics? How can you use such analysis practically? Every company after all faces the need to match names and random lists together properly to understand coherence. This Fuzzy match company names enables organizations to better lead scoring, implement better strategies and do a proper competitive analysis.
Consolidate customer information
Customer information is generally both scattered and organized. It is organized in the sense that it is properly stored on record in a proper system by each department. It is scattered because more often than not there is no collating between these departments. Multiple different departments maintain these records, for example the sales team, the finance department etc.
To make the most of this information, there is a need to consolidate all of this incredibly useful information at one place, and make it a consistent and constantly updated resource. The aim is to
- Have a resource of information which is verified and up-to-date and serves admirably in case of a real- time information need.
- Have fuzzy matching algorithms here as they can find out possible errors during collation. For example, customer, #9, #99 and #9999 are probably the same one. Fuzzy matching makes sure you are not making the same errors as made in the primary data.
Segmentation of your market
A company name matching software will help you consolidate the customer information, and this will lead to sound analytical results regarding your market.
With a Company name fuzzy matching software, you can get answers to questions like:
- What are the demographics?
- Which group seems to return to you most often?
- Which segment of customers buys the least?
Once these and similar questions are answered, a capable marketing plan can easily be drawn up. Segment your market. In each target group, draw up a profile of an ideal customer. Match this against your data. Analyze what is missing, which group responds most successfully to which kind of marketing and so on and so forth. Match company lists to obtain all this data.
Identification of likely prospects
Once you have the relevant information and segmentation of your market in place, you will be in a position to identify new target markets that have not as yet been developed by you.
You can analyze your company’s presence in these spheres, what is lacking, and how you can develop further. You can use the results from your segmentation as you will know from there that what kind of tactics work on which kind of group.
Model expected responses
Once using these advanced analytics becomes easy and an everyday affair, I suggest to model expected responses. What this means is that before you organize any marketing campaign, for example, or execute a new product idea, instead of just testing it out in the real world and not having the power to improve or change it, you can predict responses to such new things and plans.
You can use tools like propensity to buy, attrition and financial distress to measure the responses and build such a model. This will give you a fair idea of the kind of responses you may expect within a certain demographic under certain prevalent conditions. You can also employ market penetration analysis tools to help analyze your customer base as against parameters of geography, and the industry; among others. This is a very practical step to take as you can often weed out any small mistake that might have crept in, and can make the suitable changes.