Can marketers use purchasing data while respecting individual privacy?

Marketers routinely collect data to communicate with prospects and customers and inform marketing decisions. They may, for example, gather e-mail addresses and names to send a recurring newsletter to prospects or inform existing customers about a new offer.

For the marketer, data becomes most valuable when it can either be used for direct communication or when it can be aggregated, synthesised and interpreted to reveal overall trends in what prospects and customers value.

Two types of marketing data

Data used in direct communication: Direct communication with prospects and customers requires, at minimum, the contact information of individuals. To send mass mailings you need to have a list of postal addresses, whereas digital communication may require e-mail addresses, user aliases or a numeric ID.

Aggregated marketing data: Aggregated user data such as website analytics are usually used by marketers to identify broader marketing patterns, like what channels users come from, what pages are most and least clicked and which opt-in forms or landing pages have the highest conversions.

Aggregated marketing data is collected at an individual level and then sorted and organised based on specific, pre-set criteria. For example, website and e-commerce visitor data could be grouped by time/day, location, origin, or customer-specific attributes like age, gender and purchasing history.

This means that even when marketers do not use individual users’ data, IT networks are configured to gather personal data and aggregate it based on specific criteria.

Personal data is the foundation for aggregated data

For example, most web servers automatically keep a log of all requested pages, as well as unique identifier (IP address) to keep track of who is making the request. This is of course a legitimate business interest, as it can help web developers identify problems (e.g. page-specific errors, server and delivery issues, etc.) and ensure relatively secure and smooth operations, while making cyberattacks (DDOS, brute force attacks, etc.) traceable and preventable.

Which data collection tools do marketers work with?

However, marketers do not usually work with server logs for their data analysis. Instead, they may adopt their own suite of software tools for managing data, such as:

• Customer relationship management software (CMR) for storing customer data
• Analytics software for tracking website, e-commerce and app usage
• E-mail software for sending e-mails to groups of prospects or customers based on pre-defined attributes (e.g. topic, interests, past behaviour) or pre-set campaign workflows, and for gathering metrics such as open rates, click rates and purchases
• ULR parameters for tracking user behaviour and acquisition channels
• Surveying software for gathering prospect or customer feedback

This is because the data gathered through such tools may be more accessible and easier to process and analyse using the built-in functionality of such tools. The synthesising features of such tools, in particular, mean that data can be aggregated for individual users, creating individual user profiles.

With the help of these tools, the marketer may find that a 21-year-old man from Tennessee accessed the company website after clicking on a specific social media post and then visited 5 product pages before buying product A in the online shop. They may also discover that the man took particular interest in photo B on the second page he visited, as his cursor hovered over that image several times. Due to the eventual purchase, they could even link this info with his name, contact details, address and payment info to create a full customer profile.

How much personal data marketers gather and use

While the technological means to implement these data gathering and profiling practice exist – at the very least – since the mid-2010s, only a small proportion of companies have gone so far as to (effectively) implement them.

For one, such advanced data aggregation systems are challenging to set up as they require an in-depth understanding of marketing analytics, data management, as well as the potential avenues via which prospects and customers can engage with the whole range of the company’s marketing materials.

More often than not, a lack of technical understanding means that data is in fact gathered, but it either lacks in quality or cannot be aggregated effectively. The latter may occur when data is gathered across multiple different software tools (e.g. website analytics plugins, e-mail marketing tool, third-party ads platform, etc.) and not connected via an API or fed into a central analysis application.

Another reason why such detailed customer tracking is rare is because of the high cost. In order to implement user tracking from first contact to purchase, a business may need to invest in one (or probably multiple) of the following:

• Recurring subscriptions to marketing (analytics) software
• People who can manage the technical set-up and maintenance of different marketing systems
• Out-of-the-box or custom-built APIs for connecting different marketing tools
• Individual customizations that make the aggregated data valuable for the marketing team
• Data storage (i.e. a server or server network to keep all the gathered data)

This means that elaborate prospect and customer tracking is reserved for the marketing teams of larger organisations with ample funding from internal or external stakeholders.

But even with the skills, and technical and financial resources to implement such systems, it is by no means guaranteed that a business will derive value from detailed prospect and customer data and create additional value for prospects and customers as well.

What makes marketing data valuable to a business?

Marketing data becomes valuable to a business when it:

• Uncovers new markets with contact information from prospects
• Enables communication with prospects to further or abort the sales process
• Provides feedback on existing products or services that can be used to improve the company’s offer

In other words, marketing data is valuable when it increases the number of sales and their profitability.

However, the company can only achieve a decent level of sales when it delivers a product or service that its customers value.

The delivery of that offer, usually involves an interaction between the customer and the business – and therefore necessitates the exchange of some personal information.

For example, an online shop will require the postal address (or post box) of customers to ship its stock to the intended recipient, while the sales clerk in a traditional store can make note of the buyer’s (approximate) age, physique, sex and other attributes merely through observation.

Information gathered through these exchanges is, of course, just as a valuable to a business, in a promotional context. For example, knowing that many customers live in one part of the city may help the business send a mailing to residents of that district.

Or knowing that older customers typically purchase higher-value items can encourage the marketer to segregate their email list based on the approximate age of their prospects. The intended benefit for the business is more sales, while prospects will (ideally) see only those offers that they find valuable.

Why privacy matters

While the reduced amount of irrelevant advertising can make it seem as though the collection of marketing data is a win-win situation for businesses and customers, extensive data gathering also comes with certain risks and potential downsides.

These include:

• Cybersecurity threats, such as hacking, which may reveal sensitive data to the public with negative consequences for the individual (stigmatisation, blackmail, threats, etc.)
• The weaponisation of data to manipulate the perception and beliefs of the individual
• Information asymmetries leading to adverse selection, whereby unsuspecting consumers choose lower-quality offers and unintentionally push high quality sellers out of the market

Data leaks also undermine trust of customers in a business – thereby creating at least some incentive to consider user privacy in marketing.

Which marketing data provides the most value to a business?

The risk-value trade-off

The risk-value trade-off is a concept to describe the dual nature of marketing data for a business. One the one hand, purchasing data can provide economic value to a business, on the other hand, managing and looking after that data comes with certain risks.

Therefore, a business should only collect and use that marketing data for which the value is higher than the risk. This tipping point will be different for each business.

For example, a hotel may want to know the name and address of guests when they make a reservation (in order to avoid fake bookings), while a grocery store may not even need individual addresses to mail flyers to people who live in its vicinity.

Different types of personal data and the value and risks they can provide

How to identify valuable marketing data in your business

While the table above gives us a general idea of how and why personal data may be of value to a business, the marketing strategy will dictate how much value the business can derive from a specific data type.

For example, a company that primarily serves customers online – and does not rely on mail order or sales letters for promotion, may not derive much value from a shipping address, while it may benefit from knowing the purchasing history of individual customers, as that allows it to better promote new offers to its existing buyers.  

The key question any business can ask to identify if a specific form of data is valuable for marketing purposes is:

How would we have to change our marketing and promotional activities, if this form of customer data were no longer available?

The answer will show you how much (or how little) the business actually uses a particular form of data. The more the business has to change, the stronger its reliance on a particular data point. And if the change would be a net negative to the business, then it derives a high amount of value from that data – whereas if there is no significant change in earnings, costs and customer experience, the value of the collected data point is likely to be minimal.

Purchasing data: a value driver for most businesses

Across almost all industries, purchasing data is one of the most valuable forms of data that a business can collect. In its simplest form, purchasing data tells a business who bought what product or service for what price at what time and via which channel.

This data can help a business better plan and develop new offers, increase its operational efficiency and reduce costs. But in a sales and marketing context, the main reason why purchasing data is so valuable is that past behaviour often works as a predictor of future behaviour.

When a business knows what prospects are interested in, it can promote complementary offers and develop specific promotions for future offers based on an initial purchase.

Privacy considerations around purchasing data

For most purchases, a business will gather a comprehensive set of personal data including name, location, contact info, payment details, and many more. Part of this data is needed to fulfil the contractual obligation towards the customer – such as an address for shipping products from an e-commerce store. Other types of personal data such as country of residence may be required to meet legal and tax regulations.

While this data is collected in the way of doing business, the question remains to what extent marketers can (and should) use it to add value to prospects and customers, without intruding on an individual’s privacy.

How to use purchasing data while respecting individual privacy

Marketers can respect individual privacy by using anonymised and aggregated purchasing data. They can set up systems that substitute personally identifiable information (such as names and contact details) with numeric aliases and synthesise individual data points into pre-defined reports.

For example, they may look at random customer IDs to identify how often and over what time span the average customer buys their product, and what the average value of each transaction is. They could then further segment this data based on attributes such as acquisition channel, age bracket (instead of precise age), sex, location, and so on – provided they wish to target a specific group of customers or prospects in further communications.

To avoid accidental identification of individual customers, the marketer could define a minimum number of records that has to be reached before they can look at and use segmented data. For example, the minimum segment size may be set at 5% of customers, or at least 30 prospects. This also has an added benefit: the larger the sample size, the more likely it is that data is representative, meaning that marketing decisions are more likely to be based on sound and statistically significant analyses.

While anonymisation and data aggregation can help increase user privacy, there are limits as to how far a company go in terms of minimising data use and still adding value to their customers. After all, we derive value from many products, services and methods of delivery through some form of exchange, whereby the personal information would naturally be exchanged. The only difference in the digital economy is that each exchange can now be registered, tracked and analysed not only by the parties directly involved in the exchange, but by any third party that becomes privy to the exchange.

To minimise third-party interference, businesses can choose to use a decentralised marketing set-up, and you can learn more about that here.