- Initially, you can store things on Excel or similar spreadsheet software.
- But as your data becomes more detailed, you’ll need specific database software to manage your customer data.
- Ask a software and computer shop or supplier to recommend you some software, explaining your needs both present and future to make sure they provide you with the right level of complexity. You don’t want to end up with something either far too complicated or far too basic for your needs.
- Make sure your data collection spans all different departments and members of staff. Everyone should be contributing to the same document. Use CRM software to manage this.
Collecting and saving customer information is the most important thing you can do for marketing! If you know how to contact your customers, you can market to them directly and inexpensively through email, text messages, mailings, and phone calls.
Remember that song: “Make new friends, but keep the old…one is silver and the other gold.” This applies to marketing and sales! Customer data is like a gold mine.
From our experience, most small businesses don’t set up a process for how to collect, save, and update customer information, and a result of not doing that is they then have to spend a lot more money on advertising and mass marketing.
When we talk about data-generating systems, we can, however, specify which systems create data for the first time, and which don’t. A checkout register is, for example, a data-generating system, because when it is scanning products, it is also generating data files, and these files in turn tell the store which products at which time and at which price are leaving the store. When the day is over, the customer has gone home, and the register is balanced, the store can choose to delete the data in the register—but we don’t always want to do that because this data can be used for many other things. When we choose to save the information, the data-generating system becomes a source system for one or several specific data warehouses. Based on this data warehouse information, we can carry out a large number of analyses and business initiatives (e.g., inventory management, supply chain management, earnings analyses, multi-purchase analyses, etc.).
New data is, in other words, not generated in a data warehouse. Data in a data warehouse comes from somewhere else and is saved based on business rules and generated to meet the company’s information requirements. Some examples of data-generating source systems are:
- Billing systems
- These systems print bills to named customers. By analysing this data, we can carry out behavior-based segmentation, value-based segmentation, etc.
- Reminder systems:
- These systems send out reminders to customers who do not settle their bills on time. By analysing this data, we can carry out credit scoring and treat our customers based on their payment records.
- Debt collection systems
- These systems send statuses on cases that have been transferred to external debt collectors. This data provides information about which customers we do not wish to have any further dealings with, and which should therefore be removed from customer relationship management (CRM) campaigns until a settlement is reached.
- CRM systems
- These systems contain the history of customer calls and conversations. This is key information about customers, which can provide input for analyses of complaint behaviour and thus what the organisation must do better. It can also provide information about which customers draw considerably on service resources and therefore represent less value. It is input for the optimisation of customer management processes. It’s used in connection with analyses of which customers have left and why.
- Product and consumption information
- This information can tell us something about which products and services are sold out over time. If we can put a name to individual customers, this information will closely resemble billing information, only without amounts. Even if we are unable to put a name to this information, it will still be valuable for multi-purchase analyses.
- Customer information
- These are names, addresses, entry times, cancellations, special contracts, segmentations, and so forth. This is basic information about our customers, for which we want to collect all market information.
- Business Information
- This is information such as industry codes, number of employees, or accounting figures. It is identical to customer information for companies operating in the business-to-business (B2B) market.
- Campaign history
- Specifically, who received which campaigns and when? This is essential information for marketing functions since this information enables follow-up on the efficiency of marketing initiatives. If our campaigns are targeted toward named customers, and we subsequently are able to see which customers change behaviour after a given campaign, we are able to monitor our campaigns closely. If our campaigns are launched via mass media, we can measure effect and generate learning through statistical forecasting models. If this information is aggregated over more campaigns, we will learn which campaign elements are critical and we will learn about overall market development as well.
- Web logs
- This is information about user behaviour on the company’s Web site. It can be used as a starting point to disclose the number of visitors and their way of navigating around the Website. If the user is also logged in or accepts cookies, we can begin to analyse the development of the use of the Web site. If the customer has bought something from us, it constitutes CRM information in line with billing information.
- Questionnaire analyses performed over time
- If we have named users, this will be CRM information that our customers may also expect us to act on. Questionnaire surveys can be a two-edged sword, however; if we ask our customers for feedback on our service functions, for instance, they will give us just that, expecting us to then adjust our services to their needs.
- Data mining results
- These results, which may be segmentations, added sales models, or loyalty segmentation, provide history when placed in a data warehouse. Just as with KPIs, this information can be used to create learning about causal relations across several campaigns and thus highlight market mechanisms in a broader context.
- Information from ERP systems
- This information includes accounting management systems in which entries are made about the organisation’s financial transactions for the use of accounting formats. It can be related to KPI information, if we want to disclose correlations
