The significance of data in digital marketing - part 1
Data and the use of it is fast becoming an important part of our daily lives. This data allows the modern marketer to employ new digital techniques when communicating with his consumers and clients. But how do you get the most out of this data?
6 reasons why data is important
There are several reasons why data is so important, not all of them linked to marketing. I distinguish six different areas of importance
For legal obligations.
The legislator tells us that we need to keep certain data. Historically, this was primarily for accounting information that had to be kept in a standardised form. However – and more important for the marketer - since 2018, we also have to comply with GDPR regulations.
For transactional support.
As a company, you want to keep track of your inventory as well as the different purchases and sales your company makes. While not directly linked to marketing, this can still be important information for your digital strategy. For example, when there is insufficient stock of a certain product, promoting that product in your communications is inefficient and can even irritate your customers when you are not able to fulfil the demand that you have created yourself.
To make business decisions.
Data regarding the market and your performance in it will have an impact on decisions your company makes in its market approach. These decisions can be very diverse. Some are related to the conduct of business (hiring people, laying off people, automating processes, …). Others have an impact on market approach such as entering/abandoning markets or product groups, putting more or less focus on customer groups or product groups, … It is obvious that those decisions will have repercussions on your (digital) marketing strategy.
Get to know your customer.
Data allows you to build a 360° view of the people and companies you do business with. This knowledge must be used in the marketing, sales and service departments to approach your customer in a consistent way.
To personalise communication.
From a simple use of your contact’s first name to adapting communications to the needs and wishes of your contact. We will discuss this area in more detail later on.
To avoid mistakes.
Mistakes happen more frequently than they should. From getting an email in a wrong language (something that happens more than once in Belgium) to getting a promotion for a product from the company where you just bought that product. Or even worse, mails and other communications sent to an already deceased person. Especially in these times of social backlash, the effects can be deadly for your brand image. Correct gathering and use of data help prevent these kinds of mistakes.
Where does data come from?
Data comes from different sources. Some are obvious, others come from directions that are frequently overlooked while they may have a large impact on your communication with your prospects and customers.
- The main source of origin of data regarding your customers comes from the customers themselves. Whether it’s a filled in web form, an in-store purchase or an order your salesperson receives, this data is fairly easy to obtain and of high quality.
- A type of indirectly obtained information regarding your customer is his or her purchase, website and mail behaviour. This data gives a perfect insight in the needs and wishes of your customer. This can be used in various ways in your digital communications, tailoring it to those expressed needs.
- Your employees are an often-overlooked source of information regarding prospects and customers. In their dealings with your customers, they capture their often implicitly voiced needs. This informal and frequently unstructured type of data can be very powerful when properly used. The challenge with this kind of data is not the capturing itself but the structured storing, in a way to empower the marketer in exploiting the information.
- Social media can be used in your market approach. Identifying brand ambassadors can help you in steering marketing effort and budgets. But formulating a correct response to negative social messages is equally important.
- Industry benchmarks help you and your organisation compare yourself with the market and give insights as to whether you are a good performer in a competitive market or not. This benchmarking can be high-level (i.e. what is my margin compared with my competitors) where far-reaching changes in your organisation can be needed. However, other benchmarks such as comparing open rates of your newsletter give you valuable insights in your marketing efforts and allow you to adjust your communication.
- Relatively new in the area of data gathering is the Internet of Things (IOT). Sensors built into appliances are transmitting data back to the suppliers and can be used to monitor the use of these IOT-systems, but also to deliver marketing communications such as maintenance contracts suggestions, upgrade selling, etc.
The challenge in handling data
The big challenge in the handling of all this data is combining it in a way that allows implementing it for decision making as well as for operational usage. Often, these two objectives have different prerequisites, where decision-making needs aggregated data and specific business intelligence tools, while the operational usage (whether it’s meant for logistical purposes, CRM, marketing or other purposes) needs to have an integrated database with the most detail possible.
This often means that companies have two different technological platforms for these purposes. This is not an issue, as long as your operational processes have access to the operational data combined with the results of calculations obtained from your business intelligence platform.
I do notice that even today, the wealth of data available in many organisations is silo-ed, with departments completely in the blind of what their colleagues are doing. Examples of this are e-commerce companies serving you ads for items with a discount, while you bought those items yesterday at sticker price; or commercial mails you get from a company while you have a complaint that is not resolved.
What to do with all this data?
Now we have all this data; but what can we as marketers do with it? The answer is not rocket science: we all want to tailor our messages to the recipients, so that they have the feeling that we are speaking their language. A goal that can be achieved using two different mechanisms, namely segmentation and personalisation.
Due to several abuses in the last years, the usage of data has sometimes received a negative connotation. However, without data it is not possible to communicate with your contacts in the way they expect it. Consumers no longer accept communications that are not tailored to their situation. Consequently, it is important to use the data at your disposal in an ethical way, thus avoiding customer backlash.
Types of data to use for segmenting and personalising
There are different types of data that we can discern for use in segmenting and personalising:
- Attribute-based parameters include obvious information such as gender and age but perhaps also shoe size (but only if you are active in footwear). Combine this with other attributes based on work parameters (title, level, income bracket, company for B2B), relation-based information (marital status, family situation) and proclaimed info (e.g. wants to receive the newsletter) to give a good overview of the person you are communicating with.
- These types of attributes depended on harvesting the necessary information from your contacts. Some of the information will be easily shared, others might be harder to get. It all depends on the type of information you request how willing your clientele will be to part with their particulars. Some guidelines are:
- only ask for that information that is relevant to your business and the relationship (shoe size will be hard to get if you are not a footwear retailer);
- do not request all the information you would like to have at once but set up data harvesting programs where minimal information is queried for in the beginning and more details are asked for in future interactions;
- a lot of information can be inferred based on the interactions with your customer. Placed orders is an obvious source for data harvesting (coming back to footwear, the shoe size will be given on the order) but also other interactions can be used; requesting information or usage of the web site (i.e. checking the stock of shoes in a certain size) can be used to get the desired information. Make sure this is properly stored in your database for use in segmenting and personalisation.
- Geographical parameters are also attributes, but I want to differentiate these from the category above. Geographical information can be used to segment based on countries or regions, on type of area (inner city vs. suburb or rural) or on physical shop reach (inviting all your contacts within a 10 kilometre radius of a given shop to an event).
- Behaviour-based parameters are based on the interactions your customer or prospect has with you. Of course, purchasing is an obvious behavioural interaction that can be used for cross-selling products and services. But also email interactions (opening and clicking through) and website interactions can be very useful indicators of purchase signals. These behaviours can be used for time sensitive marketing programs (e.g. where abandoned basket emails are sent when a purchase is not completed) or for assigning contacts to a certain group (e.g. early adopters).
- Location-based data (with location used in the broadest sense, including ‘virtual location’) is important information with a very limited shelf life, that should be used immediately for marketing programs but should not be stored per se. Difference with the geographical data as described above is that the former is persistent data (people do not move every day) while this location-based information is on-the-spot information: where is our customer now? Examples are geofencing, where promotions are pushed to the app based on the customer’s proximity to a brick-and-mortar store or the display of banners during the visit to your website. However, the information obtained could be made persistent in some cases, such as preferred shop based on geofencing.
- Aggregated data will combine information from several sources from a variety of sources resulting in a relative appreciation of a given contact. A common aggregation used in retail is the RFM (Recency, Frequency, Monetary) model, assigning three gradings to each person in your database, the most valuable being a AAA-customer. In B2B and high-value consumer-goods environments, different scoring models can and will be used that assign a grade to a contact in relation to the chance of purchasing your goods. This score is based on points gained for attributes of the customers (e.g. position in the company, with a purchase director having a higher importance for you than an administrative profile) combined with behavioural points gained (website visits, time spent on website, whitepaper downloads, physical meeting with salesperson scheduled, input from the sales department, …). The higher the point score, the more likely that this contact (or company for B2B) is a valid marketing lead that can be converted to a sales lead and the more effort and resources that should be dedicated to the opportunity.
- External data is data you do not store in your database, but which is inferred based on existing data for your contact. Examples of external data is weather information based on the place of residence of your contact, traffic information based on the current location of the contact or nearby shops (also based on place of residence or current location)
Based on all this, it is obvious that the capturing, storing and utilisation of data is extremely important in any organisation. While being a multi-departmental discipline, the marketing department is a very important ‘user’ of the data in its quest to capture the attention of the customer.
In part 2 of this blog about the significance of data in Digital Marketing, we will cover the use of data in segmentation and personalisation and also talk about Artificial Intelligence with regard to data.
Get more out of your data
With its vast array of expertise and skilled resources, Intracto can help you with the implementation of your digital data strategy in the areas of CRM and marketing automation.Contact us