In various contents I talk about CRM (customer relationship management) as a database for potential customers and clients, even if the name doesn’t make it clear. Because in fact it also has other functions that go well beyond data storage and therefore goes well beyond statistics understood as a discipline of data collection.
MySQL server represents a general case of database, CRM a special case. Just like the “space” you purchase through the services that have you doing electronic invoicing, that space comes from a server, which is used to store invoices and more. Hubspot, not surprisingly, uses MySQL as its database. In this article you will read about this tool with more of a statistical-informatics slant than a technical or marketing slant.
There are paid CRMs, free up to a certain point, and open source, but this does not mean free in the strict sense, because there would be management costs, expertise costs, etc. The provider that hosts your company website could allow you to install vtiger, an open source CRM, on your subdomain. If companyname.com is your domain, mycrm.companyname.com is your subdomain. There are other solutions like vtiger: espocrm, odoo, mautic and if you want you can host them on some server, company PC, if the team has the skills for this “do it yourself”. Making a comparison between solutions of this type and paid ones goes beyond the scope of this article.
From a statistical point of view, however, the ease with which we can retrieve data from their servers, via a data waiter, or API, becomes important. Certainly services such as Zapier, Make, reduce or negate this difficulty, assuming the connector exists for the specific CRM, but there are also solutions from data engineering.
Let’s see some examples of data that can be seen on CRMs, understood as a table column, and some statistical benefits:
- Contact ID: useful if they make multiple purchases
- First and last name: you can use APIs to figure out gender, one more piece of information about the target customer.
- Email address: a lot of information (maybe too much) can be extracted from emails, but be very careful about the European General Data Protection Regulation (GDPR). Thin line between statistics and public-source espionage (OSINT) that can lead to illegality.
- Phone number: same as above.
- Address: aggregations on this front can turn up surprises, especially when cross-referencing this data with socio-demographic data in the area.
- Company: again, doing data enrichment on companies can bring out very interesting and useful recurring patterns. This case applies to those who sell to companies (B2B, such as Enterprise Statistics). For example, a certain macro group of SIC code buys more.
- Job position: again, recurring buying patterns can emerge for certain occupations. Not always easy to obtain.
- Source (where the potential customer or lead comes from: e.g., website form, Facebook ads, etc.): doing aggregations on this front helps to understand one’s ideal customer or understand where those who consider us their preferred supplier come from
- Status of the lead or status of the negotiation
- Value of the opportunity, understood as potential revenue
- Last contact (e.g., message, email, call)
- Notes. Very important unstructured data element. About which you can argue with salespeople who have the perception that they are throwing time into manually filling in this field.
- Tags (for segmentation, can be derived from previous points)
- Lead score: helps prioritize the contact in some cases, serves more for salespeople than anything else, but knowing the purchase path of customers makes this column quite useless, in my opinion.
- Date of creation (of contact): if you have enough data, seasonality can emerge (all things being equal)
- Conversion date: through the previous point, you can create the indicator “Conversion time” and condition it at will with other variables, for example: who has “Website” as a source converts first?
- Contact owner: in some companies new contacts are assigned to a seller following rules. Statistically you can see which salespeople sell the most and/or adjust the incentive system
- Interaction history: be careful because counting interactions and using this new variable as an explanatory variable to understand who converts, becomes a confounding variable
- Touchpoints: helps understand the path to purchase: you can build state-pass matrices, a statistical model that calculates conversion probabilities as a function of contact characteristics, and more
Typically CRMs have one table, or data set, for prospects and another for customers.
And of course you can create custom columns, kind of like in Meta reports for advertisements. But in CRMs you can also create custom columns that call external services (again via API). A trivial example? Contact’s hair color: I retrieve a photo (1st service) and a computer vision algorithm “reads” the hair color (2nd service). Useful for tattoo artists and some psychologists?
CRMs, as mentioned, go beyond collecting and storing certain types of data. Many of them also have reporting. I recommend using the internal CRM reporting if you have no one in your company team who knows how to create pivot tables, or if you do not want to hire Enterprise Statistics to enhance your CRM from a statistical point of view. Having a CRM without reporting, and therefore without some data dashboard, becomes quite useless.
So you have understood the importance of CRM and some of the benefits, also with a view to having a data-driven or data-informed company, as it can also decrease customer acquisition costs: it is an important digital asset for corporate data culture, especially for SMEs, as well as having a transformative functionality. Remember, however, to use services that comply with GDPR or territorial equivalent, because this database collects personal data.
Maybe you don’t know, you might not have a “traditional” CRM but in a different form: an Excel file or a Google spreadsheet. Even in that case Enterprise Statistics can enhance that data and advise if it is better to use a more usual form. We can discuss this in a free call of no more than an hour.