The best data strategy behind CRM to boost your business
The best data strategy behind CRM to boost your business
1. Data, information, and CRM
Information is a data set meaningfully processed according to a given requirement, resulting in understandable tables, listings, and graphs.
Thus, data is raw strings of information that convey very little information, like pieces of a puzzle that, until they are put together, are difficult to understand fully.
Taken to a CRM, the data is the basis of the information contained. From an element that sustains a fragile structure on which the quality of commercial management depends and, therefore, the present and future sales of a company.
It is, therefore, essential to focus our energy on the base of the structure if we want to improve the business strategies that come out of the CRM. Depending on the quality of the data, we will obtain a proportional quality of information that will allow us to make more effective decisions.
2. Data quality and consumption
The data life cycle
Before its consumption in the CRM, the data must go through different stages. These stages are processes that guarantee the maintenance of data quality until its use.
Know what to look for and how to look for it. We can talk about identifying internal or external sources for organizations depending on the needs.
It is a phase that can involve several departments f, from legal & compliance to data protection issues or IT for interconnections between systems and information security.
If the data comes from different sources, normalization will likely be necessary to homogenize its use and allow aggregation and correlation operations.
The labeling of the data can be similar to its naming or classification. From there will come the data’s role for the information, want it to link.
It could be the most imessentialart of data processing. —throw connects with the other data for the transformation into useful information.
It will be necessary to store it temporarily for its transformation into information.
We can share the data when labeled or hope it is already part of a set of information. The ability to offer channels for data consumption is necessary to exploit its full potential across the entire organization.
Some data loses value over time. To avoid this, search and update processes are applied to each piece of data.
By the previous analogy, working in all stages of the data cycle is essential to obtain all the pieces of the puzzle.
However, CRMs, by their nature, connect with data sources but are not data processing software. Data sources on their side generate raw data that usually processes the data just enough to transfer.
Consequently, data sources are usually connected directly with the places of data consumption, transferring inaccurate or low-quality data.
Data consumption Data as a Service (DaaS)
The different departments of a company have different needs in terms of what data they want to consume, how they have to finish it, and the amount they need to improve decision-making.
Data as a service or DaaS was born to respond to the different data consumption needs. The term refers to cloud services that provide a flexible infrastructure to connect with varying sources of data and data consumption applications such as CRM to use data with agility and precision.
But not only that, it also provides a perfect environment for storage, processing, and analysis, improving the data cycle in all its stages.
The main advantage of DaaS is the ability to link data from different environments and ecosystems to create personalized information based on the needs of each data consumer.
- Data always up to date
- Consume only what needed
- No need for a very complex or expensive infrastructure
- quick commissioning
- optimized cost
- business oriented
- Allows implementing of Data Ops methodologies
3. Data quality and consumption
Let’s see an example that allows us to understand better everything discussed above.
The company Mas deporte.SL is a sporting goods brand that has several stores in Spain. Its sales trend had been flat for some time, and as a result of a solid global economic decline, turnover began to decline gradually
The brand used a CRM as a customer directory. They had a picture of what the business relationship was like. Still, they had difficulties obtaining the necessary insights to optimize the strategy based on the data contained in the CRM.
Why weren’t they able to take advantage of their CRM?
They had a single source of data when they implemented the CRM from their loyalty card.
In addition, after the implementation and as a result of marketing campaigns to try to grow sales, 2 other data sources were generated:
- Coming from sporting events where anyone could test the brand’s items and get prizes and discounts for winning sports competitions.
- Coming from contests and raffles that they did on social networks with elite athletes and sports influencers.
This situation generated several problems in the CRM:
- With the implementation of the CRM, the automatic integration of loyalty card data configured. However, the two data sources added after the fact did not fully consider the CRM data model. Due to the amount of data and complexity within the model this resulted in outdated data and duplicate customers with the address field in different formats.
- They had difficulty segmenting and obtaining reliable extracts from the database to analyze
- They did not have enough data to carry out advanced profiles of clients and potentials. And the commercial campaigns were very generic with low return due to the impossibility of personalizing the messages
- They had outdated data on their business clients, in terms of number of employees, main activities, addresses…etc.
In short, due to a lack of quality and precision in the data, the analyzes were meaningless and will not allow conclusions to be drawn that would improve decision-making. A CRM with bad data leads to a bad Customer Experience that reflected in business results.
How they strengthened their data strategy to get the most out of their CRM
After working on the normalization and standardization of the CRM database, data quality pre-processes were generated for all the data coming from the sources that generated duplicate and non-standardized data before being uploaded to the CRM.
Secondly, through an API connection and a DaaS service, they connected the CRM to Pyramid, DataCentric’s third party data . Having normalized the customer address field, it was possible to add more than 2,500 indicators of the different geographical areas to which their customers belonged. Data at the census tract level such as:
- Household structure
- Distribution of health spending
- Gross income and disposable income
- Surface of the houses
Once the CRM was enriched new information, the analysis team began to work on an advanced profiling of clients, combining the new data the existing data on the sale of items.
The results of the analysis made it possible to obtain valuable information on, among other things , the socio-economic level, the importance of health, the type of housing or the type of family/home . What ultimately allowed in record time:
- Generate different recruitment, growth and loyalty strategies focused on different customer profiles with different needs
- Establish new onboarding processes based on the client’s area and their propensity for certain products
- For those more expensive products, launch complementary financing services based on the risks of non-payment
- It allowed an analysis of the store network to see cannibalization areas between stores and to be able to optimize costs
- Produced the discovery of new potential areas in which the brand could focus the commercial effort with new stores and promotional actions
- The implementation of personalized offer solutions based on data in your e-commerce
These solutions have been key in a 28% increase in new customers and in being able to increase the average purchase ticket by 17%
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