Growing Customer Lifetime Value with data

Targeted marketing communications have been available for decades but have been rather exclusively available to only those organizations with the best of systems and most importantly, the best of data.

You’ll want to consider personalized communication to improve customer retention, augment and assure customer satisfaction, support and facilitate cross-selling, and ultimately drive greater customer lifetime value (CLV).

Plenty of market research evidence suggests that personalized communications materially cement and augment the opportunities for an organization to galvanize a strong long-term relationship with the customer and accordingly drive greater CLV.  

with augmented data, there is also now the possibility to identify who the lower and higher-value customers are and identify characteristics and traits that allow improved segmentation leading to greater value growth potential.

When these same augmented data sets are combined with tools like machine learning and artificial intelligence to build predictive models, one is able to formulate strategies to migrate members of lower-value segments or groups to the higher-value ones.

The important fact to consider here is that personalized communication may be the important key. Once you’ve singled out members of lower-value segmented groups, and then embarked on communicating with them in a highly personalized way through direct marketing campaigns, only then are you likely to be able to promote them to the higher groups.

The marketing campaigns of course are not limited to letters in the mail, or personalized emails. Your product marketing mix would also leverage the other elements in the marketing library, namely social media and personalized web experiences. The objective here is to leverage the data to hone the product or service marketing, promotion, and advertising messaging to be very distinctive and almost unique for each and every customer.

Now more so than ever before, the smallest organizations can start to consider the use of the same tools and methods that much greater and older organizations have had at their disposal. Globalization and ready accessibility to the web has meant that any product can be sold almost anywhere where delivery services are possible, further, transparency in pricing, internet search, and aggregators like Expedia, Amazon, Etsy, eBay, and the like mean that customers will do price comparison.

The same products can also be offered in price-differentiated marketplaces if you know who you’re targeting. These benefits also mean that the competitive environment is also greater. Steps need to be taken to differentiate on message and service rather than relying exclusively on the product itself and possibly the dependence on the brand.

In the end, the strength of the relationship that your organization has directly with the customer will provide the best possible predictor for the CLV.

There are many affordable customer relationship management tools available including SaaS-based pay-as-you-grow offerings. Using these together with aligned Master Data Management solutions you are able to analyze and target the right customers and prospects with the right offers.

CLV and in turn, the profitability of each customer leads to measures of value that will be different for different customer groups. The identification of efficiency in the way the value is produced helps to direct an organization’s efforts.  This in turn leads to the refinement of the exact kinds of customers and business partners that your organization wants to expend effort and energy on. The profitability of the customer is the revenue derived from that customer less the costs of identifying, securing, retaining, and growing them. The same approach needs to be used for campaigns. The more compelling the proposition with the greatest possible efficiency, the highest potential profit can be secured per customer.

At Pretectum we believe that these insights and decisions can only be obtained and undertaken from the presence of good and comprehensive master data and we present the Pretectum CMDM as one of the ways to achieve that good master data from origination through controlled curation and distribution.

Your customer data in the cloud

The security of customer data in the cloud depends on several factors, including the security measures implemented by the cloud service provider, the security controls in place for the specific data and the configurations set by the customer.

When customer data is stored in the cloud, it is typically protected by a number of security measures, including: network, physical, encryption, access control, monitoring and auditing. When considering how you manage your customer master data, consider how all these aspects are being handled.

Cloud solutions like the Pretectum CMDM applications are housed in data centers that are secured with access controls, surveillance, and other physical security measures; we make use of the some of the best in class implement network security measures, such as firewalls and data encryption to protect your customer data as it is transmitted over the internet. This data is encrypted both at rest and in transit, to protect it from unauthorized access.

Access to your tenancy and actual data is strictly controlled through role based access controls (RBAC) to ensure that only authorized users can access customer data. In addition, we monitor and audit our systems for security incidents and the potential of attemp[ts to gain unauthorized access to customer data.

Pretectum believe that customer data stored in the cloud is often more secure than data you house in your own systems due to the investment that we make in security infrastructure and personnel, and the implementation of security controls and processes that are designed to protect customer data.

It’s nonetheless important for you to carefully evaluate the security measures we use, and implement additional security controls as needed according to your specific requirements.

Adoption of SaaS based CMDM like the Pretectum CMDM has a number of advantages over on-premise or even private-cloud MDM.

SaaS MDM is typically more affordable as it operates on a subscription-based model and eliminates the need for expensive or dedicated hardware and IT infrastructure The intent with SaaS MDM is to also easily scale up or down to meet changing business needs, making it an ideal solution for organizations that are growing or experiencing changes in the volume of their data.

This approach is also easier to implement and typically require minimal setup and configuration, allowing your organization to quickly realize the benefits of a centralized data management solution. We also continuously update and improve the solution, meaning that our customers always have access to the latest features and security enhancements.

The Pretectum CMDM is also accessible from anywhere with an internet connection, making it easy for employees to access and manage data from different locations and reference the centralized repository for customer master data, improving the quality and accuracy of data across the organization.

To learn more about how you can take advantage of the Pretectum CMDM, contact us to take advantage of a free trial evaluation.

Taking air travel and loyalty to new heights

Airline loyalty programs have become a ubiquitous feature of the aviation industry globally. Most airlines, even the budget carriers, offer loyal travelers a range of benefits, from earning miles towards future flights, to access to airport lounges and priority boarding and seat selection.

But where did it all start? Which airlines were the first to introduce loyalty programs, and how have they evolved since then?

British Overseas Airways Corporation (BOAC), which operated from 1940 to 1974, did not have a formal loyalty program as we know them today. However, the airline did offer various incentives to its frequent flyers and regular customers.

For example, BOAC’s “Comet Club” was a social organization for the airline’s most loyal passengers, which offered exclusive access to special events, promotions, and personalized service. Additionally, BOAC provided perks such as preferential seating and priority boarding to its regular customers.

Concorde, the supersonic jet operated by British Airways and Air France, did not have a formal loyalty program. However, the airlines did offer various incentives to their most loyal customers to encourage repeat business and maintain customer loyalty.

For example, British Airways Concorde Room at London Heathrow airport was an exclusive lounge for Concorde passengers and elite frequent flyers. The lounge offered a luxurious experience with personalized service, fine dining, and other amenities.

Additionally, both British Airways and Air France provided special benefits to their top-tier frequent flyer members, such as priority boarding and access to premium lounges. While these perks were not specific to Concorde travel, they may have been particularly appealing to customers who frequently flew on the supersonic jet.

It’s worth noting that the concept of modern loyalty programs, with their points-based reward systems and other features, did not become widespread until the 1980s and 1990s when American Airlines introduced AAdvantage, the first frequent flyer program.

This was a groundbreaking innovation, offering customers the opportunity to earn miles towards free flights based on the distance they flew. The program was an immediate success, and other airlines quickly followed suit, with Delta introducing its SkyMiles program in 1981, and United launching MileagePlus in 1983.

Air Canada Aeroplan: Air Canada launched its Aeroplan loyalty program in 1984, making it one of the first airline loyalty programs in the world. The program offers rewards for flights with Air Canada and its partners, as well as for purchases made with Aeroplan’s partners.

British Airways Executive Club: British Airways launched its Executive Club loyalty program in 1985. The program offers rewards for flights with British Airways and its partners, as well as for purchases made with the program’s partners.

Qantas Frequent Flyer: Qantas, Australia’s national airline, launched its Frequent Flyer loyalty program in 1987. The program offers rewards for flights with Qantas and its partners, as well as for purchases made with the program’s partners.

Air France-KLM Flying Blue: Air France and KLM merged in 2004 and launched their joint loyalty program, Flying Blue, in 2005. The program offers rewards for flights with Air France, KLM, and their partners, as well as for purchases made with the program’s partners.

Lufthansa Miles & More: Lufthansa, Germany’s national airline, launched the Miles & More loyalty program in 1993. The program offers rewards for flights with Lufthansa and its partners, as well as for purchases made with the program’s partners.

Pan American World Airways (Pan Am) also had a loyalty program called Pan Am WorldPass. It was also launched in the early 1980s and allowed members to earn points for flights on Pan Am and its partner airlines. Members could redeem their points for free flights, upgrades, and other rewards.

These early loyalty programs were relatively simple, offering customers the opportunity to earn miles towards free flights, with additional benefits such as access to airport lounges and priority boarding added later. However, as the airline industry became more competitive, loyalty programs became increasingly sophisticated, offering a wider range of benefits and rewards to customers.

Today, airline loyalty programs are complex systems, with multiple tiers and a range of benefits designed to incentivize customers to fly more frequently and spend more money with the airline. These benefits can include free flights, upgrades, lounge access, priority check-in and boarding, bonus miles, and discounts on hotel bookings, car rentals, and other travel-related services.

One of the most significant developments in airline loyalty programs in recent years has been the growth of the internet and the rise of travel aggregators such as Expedia, Kayak, and Skyscanner. These platforms allow customers to compare prices and book flights from multiple airlines, making it easier than ever to find the cheapest flights.

This has presented a challenge for airlines, as they have had to find ways to compete with these platforms and maintain customer loyalty in a highly competitive market. One way they have done this is by partnering with travel aggregators, allowing customers to earn loyalty points when booking flights through these platforms.

Another approach has been to make loyalty programs more flexible and transparent, allowing customers to earn and redeem points across a wider range of airlines and travel partners. For example, many airlines now offer co-branded credit cards that allow customers to earn points towards flights and other rewards when making purchases outside of travel.

Some airlines have even gone further, offering loyalty programs that are not tied to flights at all. For example, Air France-KLM’s Flying Blue program allows members to earn points through a range of activities, including hotel stays, car rentals, and dining.

However, while airlines have been successful in adapting their loyalty programs to the changing market, travel aggregators continue to pose a threat to these programs allowing customers to compare prices and book flights from multiple airlines, these platforms make it easier for customers to find the cheapest flights, regardless of loyalty programs.

To counter this threat, airlines have had to find ways to make their loyalty programs more attractive to customers. This has included offering more personalized rewards and benefits, such as free flights and upgrades based on individual travel patterns and preferences.

Airlines also leverage the power of social media, with many airlines offering special promotions and rewards to customers who engage with their brand on platforms such as Facebook and Twitter.

By analyzing customer data, airlines can gain insights into customer behavior, preferences, and purchasing habits. This data can be used to personalize offers and rewards to customers, which can help to increase loyalty and encourage repeat business. For example, if an airline knows that a customer frequently travels to a particular destination for business, they may offer that customer bonus points for booking a flight to that destination.

Customer data can also be used to identify trends and patterns in customer behavior, which can inform marketing and business decisions. For example, if an airline notices that a large number of customers are booking flights to a particular destination during a certain time of year, they may adjust their pricing or schedule to better serve that demand.

Overall, customer data is critical to airline loyalty programs because it allows airlines to better understand and serve their customers, which can lead to increased loyalty and revenue.

If your business believes a loyalty program will help with growing your customer relationship then you’ll need a good source for customer data – Pretectum CMDM can help with your customer master data management needs. 

Customer master data management – the data types

Depending on the nature of your business and the relationship that you have with your customers you may have several different types of customer master data that you choose to manage and maintain. There are other types of customer data that you may need to manage too, data types that you don’t necessarily always think of as master data but which may benefit from being stored and retrieved centrally as required.

Quantitative and Qualitative

Typically, you can think of customer data as falling into one of two camps, either qualitative or quantitative. So what is the difference?

If you think of this in the context of survey data, for example, quantitative data is values that are representative summations or counts. Each data element is exclusively numerical. The values are quantifiable and can be used in statistical and mathematical analyses and calculations. They’re certain quantities, amounts or ranges. Typically they are also accompanied by measurement units such as kilos, years, metres etc. In the case of say, the height of a person. It makes sense to set boundary limits (validity ranges) to such data. It may also be useful to apply arithmetic operations or calculations to that data, like converting to an alternative unit of measure.

As for qualitative data, in master data for customers, in particular, this may be data values like sizing standards – S for small, M for medium and L for Large and so on. Others might be colours, preferences, types assortments etc. As long as numbers are not assigned, even though they may equate to numbers, this is likely considered qualitative. Similarly, the country, state or province in which a person claims to be a resident is qualitative because the indicator is an attribute characteristic.

Now you might ask, why would I care about whether this is quant or qual? The answer may lie in how you do your segmentation and reporting. When you are dealing with numeric values it is a great deal easier to create reports and extracts of data and to select ranges in particular if you are using quantitative data. You could for example have people defined as young, youthful, middle-aged, old or ancient but that’s not as useful as knowing the actual age. This is particularly important if I want all people over 18 and less than 80 for example. In addition, I could potentially calculate who these people are if I know their date of birth.

Declared and Inferred

Now that we have distinguished between quant and qual, it is worth also considering data that is inferred vs declared. Declared data is much more definitive, much more absolute at a point in time when compared with inferred data. Often the declared data is provided by the customer themselves or acquired from a system or provider that they may have provided that data to.

Inferred data is conversely derived. It may, for example, be the result of combining data from one or more sources and then effectively joining the dots between data points to draw some sort of conclusion.

Again, you might question, why you would care. The main reason might lie in the fact that your business needs to know the basis on which you hold and maintain customer data and then also consider how you arrive at decisions based on that data.

Data privacy matters continue to evolve, from Europe’s General Data Protection Regulation (GDPR) to the UK’s equivalent and Brazil’s Lei Geral de Proteçao de Dados (LGPD), Australia’s Privacy Act, the California Consumer Privacy Act (CCPA), Japan’s Act on Protection of Personal Information, South Korea’s Personal Information Protection Act, Thailand Personal Data Protection Act (PDPA), New Zealand’s Privacy Act, India’s Personal Data Protection Bill (PDPB), South Africa’s Protection of Personal Information Act (POPIA), The People’s Republic of China publicly released draft of the Personal Data Protection Law, PDPL and several others. It is important to recognise the approach that your business has to data collection. Data, personal data in particular is an emotionally charged topic with changes in consumer opinion ever constant.

Declared Data willingly shared by the user through form-fills, cookie opt-ins and submissions through social media accounts often carries the highest value to different aspects of your business as this data is 100% based on the customer or prospect’s activity. Ideally, you will also have gathered a consent indicator from the person who provided this data, which can help inform you on how this data can be used. Optimally, error-free barring deceit on the part of the submitter, you may use this data to determine appropriate access to certain products and services that you offer.

Inferred is often considered amongst the most contentious of data because as the name suggests, you’re joining dots. Data like this is engineered or developed. It has been created without express input from the person that it relates to, it may be systematically generated based on transactions, or activity. This data is neither better, nor worse than declared data, it is simply different and is most contentious, often because it is calculated algorithmically and is based on assumptions, perhaps well-informed, but nonetheless, not declared by the person that they relate to.

You might have a customer who declares that their preferred beverage is coffee but you often see tea or chai in their orders. Does that tell you that they lied? Well no, they have declared their preference to be coffee but they may often order other beverages for others in their party and that’s why you’re seeing an anomaly. You have derived a preference perhaps, from all their known transactions but that doesn’t make your inferred data correct.

So, you have these classes of data, but now let’s consider other aspects of the data that you may have.

Basic data and the rest

This is a very subjective discussion. The main reason is that what one company may consider basic customer data may be considered much more than another company may need. The decision as to what you choose to create and maintain and the reasons you may define the data in different ways may vary wildly from the needs and expectations of a competitor or another industry or even a use case within your organization.

Pretectum’s Customer Master Data management system (CMDM) doesn’t prescribe what you should or should have as that basic data definition, it is entirely up to you. While we may offer some standard models (schemas) and your systems may have specific minimum requirements, those can be supported but the end decision is up to you.

A coffee shop may only need an email address and a first name to maintain their loyalty card or loyalty system. A retailer on the other hand may require a full customer name, phone number, email address, gender, and addresses in order to make deliveries etc. A bank may require much more, not just for the purposes of giving the customer a great experience, but also because it may be a legal requirement.

When you embark on the implementation of your master data management solution, these are some of the aspects that you might want to consider.

Either way, Pretectum’s CMDM is able to support you in leveraging all of these different ways of deciding what to store and what the criteria are around the values that you might store, qualitative, quantitative, declared or inferred. All types of data are supported in a highly configurable way according to your specific business needs.

If you’re wondering whether you can achieve a particular outcome in your use of the Pretectum master data management system to improve sales, support a particular initiative or simply provide your customers with a great personalized customer experience, reach out to us and we’ll tell you what you need to know about how the system can help.

10 CUSTOMER DATA ROLES IN CUSTOMER RELATIONSHIP MANAGEMENT

Customer data plays a crucial role in customer relationship management as it provides valuable insights into customer behavior, preferences, and interactions with your organization.

Having accurate and up-to-date customer data can help your organization better understand consumers and provide personalized experiences that foster loyalty and improve satisfaction and confidence in your products or services.

Harvard Business Review contributor and business expert Barbara Bund Jackson has found this is very valuable through her research.

Here are a few ways in which customer data can impact your relationship with the customer:

Personalization: By making customer data an integral part of as many customer journeys as possible, you’re able to tailor customer interactions, providing experiences that are targeted and specific to the individual customer needs and interests.

Increased efficiency: Customer data can help to streamline your business processes and help to make better use of your resources. Routine task automation like these can benefit from a direct relationship with actual customer data

Marketing and Sales Lead scoring: Automated lead scoring systems use algorithms to evaluate leads based on their likelihood of becoming customers, and prioritize them for follow-up by sales teams. Here you might use past transactional history or simply the attributes of the customer like age and gender but through zero-party-data may be able to hone your lead scoring further.

Marketing automation: Marketing automation tools automate routine marketing tasks, such as email campaigns and social media posts, and allow companies to personalize marketing efforts based on customer data. Here, simple knowledge of the customer name, their address and other contact information, is helpful. This is especially true if that data is maintained by the customer through self-service.

Customer service automation: Customer service automation tools, such as chatbots and self-service portals, allow companies to automate routine customer service tasks and provide faster, more efficient customer support. Integration with known customers and their data, can make the dialog and interaction much more personal and directed.

Sales automation: Sales automation tools automate tasks such as lead management, opportunity tracking, and sales forecasting are used to free up sales teams to focus on more high-value activities. The more data there is in the customer master, the more effective these automations are.

Workflow automation: Workflow automation tools are used to automate repetitive tasks and processes, such as the routing of customer inquiries to the appropriate team members, freeing up time for customer-facing employees to focus on more high-value activities. The more attributes you attach to the customer master the more precision and control you have in directing these interactions and workflows.

Improved decision-making: Customer data provides valuable insights into customer behavior, preferences, and feedback, which can help companies make better-informed decisions about product development, marketing, and customer support.

Better understanding of customer needs: Customer data can help companies better understand their customers’ needs, pain points, and areas for improvement, which can lead to improved customer experiences and increased customer satisfaction.

Increased customer loyalty: It should come as no surprise that Pretectum thinks loyalty is so intrinsically linked to customer data, that we use #loyaltyisupforgrabs prolifically in social media. By providing personalized experiences and demonstrating a commitment to understanding and meeting the needs of their customers, companies can increase customer loyalty and reduce customer churn.

Customer data is a critical component of effective customer relationship management and can have a significant impact on customer relationships, customer satisfaction, and business success. Pretectum feels a CMDM is the best way to serve up the data to business applications.