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How to generate more revenue with personalisation across the 4Ps

The Data Handbook

How to use data to improve your customer journey and get better business outcomes in digital sales. Interviews, use cases, and deep-dives.

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Кatja Skachkova

Martech

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The ocean turns blood red; fewer ad platforms dominate the market, while more and more "black box" solutions determine who, where, and how your product is seen. Customers' attention spans are decreasing, and banner blindness feels like a genuine diagnosis. Simultaneously, content creation has become simpler than ever. 

In such a business ecosystem and competitive landscape, personalisation can become a key factor in staying ahead of the curve and paramount in the search for a competitive advantage and increased revenue.

In this blog post, we will discuss how personalisation can assist midsize and large businesses in driving more revenue and profit. We'll look into some examples of how personalisation can become a part of your marketing strategy, touching every facet of the classic marketing tool, the 4Ps framework (product, price, place, promotion).

The Power of Personalisation

Personalisation encompasses a wide spectrum of meanings. Gartner defines personalisation as "a process that creates relevant, individualised interactions between two parties designed to enhance the recipient's experience". According to McKinsey, in 2021, 71% of consumers expect companies to deliver personalised interactions, and 76% get frustrated when this doesn't happen.

I like to think about personalisation as a hyper-focused marketing technique that delivers the right message about the right product at the right price, in the right place, at the right time, and to the right person, creating additional value for customers. 

I like to think about personalisation as a hyper-focused marketing technique that delivers the right message about the right product at the right price, in the right place, at the right time, and to the right person, creating additional value for customers. 

In theory, personalisation meets customers' needs on an individual level. In the realm of data science, as applied in marketing, it becomes less of a theory and more of a reality. But due to simple limitations such as the complexity of data modelling and access to customer data, most businesses focus on personalisation at a group level. Nevertheless, by tailoring offerings (product, price, promotion, and place) to serve the unique needs of the targeted group exceptionally well, companies can still achieve heightened success within their chosen market, gain a competitive advantage, and seize more sales opportunities.

On this note, I must highlight or remind you of what personalisation is not. Personalisation is not a business strategy in the traditional sense (M. Porter generic strategies). Doing personalisation does not mean serving all individuals and catering to all needs. Trying to do everything for everyone means doing nothing well.

Embracing personalisation across the 4Ps

Let's take a closer look at some examples of existing personalisation solutions across the 4Ps marketing mix framework. When implemented across the 4Ps framework, personalisation transforms traditional marketing practices into dynamic, customer-centric strategies.

1. Does product personalisation work?

In the 4Ps framework, the product represents the actual offering or product that a company provides to meet the needs and wants of its target market. It includes product features, quality, design, branding, and packaging considerations. Product personalisation involves adapting products to match customers' unique needs and preferences. This can take the form of either custom-made products crafted for a particular customer or made-to-order products produced through a standardised manufacturing process, offering various options within predefined specifications.

In my opinion, this type of personalisation is the most complex and expensive as it requires customisation in the production and logistics parts of the value chain. Some examples of e-commerce businesses that actively provide customised products are Farfetch, Converse, and Marimekko.

One can argue that product personalisation in the world of e-commerce also involves recommending products/merchandising to customers based on their past behaviour, preferences, and browsing history. It allows you to showcase the right products to the right customers at the right time, significantly improving the chances of making a sale. This type of personalisation is a combination of product and promotion. It is probably the most commonly used and supported by major CMS providers and platforms such as Relevize, Nosto, Klevu, Adoric, Coveo.

2. Is price personalisation possible?

Price personalisation can take different forms:

  • First-degree personalisation using individual customer characteristics.
  • Second-degree personalisation based on factors like the quantity of products purchased (e.g., discounts for buying in bulk).
  • Third-degree personalisation where pricing depends on market segments or consumer groups (e.g., student discounts).

A recent study by the IMCO committee states that price personalisation has the potential to benefit both businesses and consumers, but it also comes with challenges. It demands advanced data collection and processing methods and might result in consumer backlash. It's worth noting that consumers generally have a negative view of first-degree price personalisation, viewing it as unfair, unlike second and third-degree personalisation, which they are more accepting of.

Personalised pricing is generally permitted under EU law, as long as businesses adhere to anti-discrimination laws and do not misuse personal characteristics.

While technically possible, the actual occurrence of first-degree price personalisation is debated. Some studies have yet to find evidence of it in online pricing, but other research and news reports, like the recent case with the Wish online platform, have shown that this type of price personalisation does occur in some instances.

Second-degree and third-degree price personalisation are often linked to price elasticity. Price elasticity is mostly influenced by factors like the availability of substitutes, the proportion of the consumer's budget that must be allocated to purchasing the item, the degree of necessity, and brand loyalty.

For the sake of clarity, it's crucial to separate personalised pricing from another strategy called dynamic pricing. In dynamic pricing, prices for the same products or services may vary online. However, this variation isn't because of consumer information but is instead influenced by current market conditions and demand. Price monitoring and optimisation are provided, for example, by Price Shape, Price2Spy, Pricefy.io, and many other players.

Many larger e-commerce companies have successfully utilised price elasticity analysis, some price personalisation, and dynamic pricing to boost their profitability. Amazon, as a prime example, regularly fine-tunes its prices by considering shifts in demand, competitor pricing, and customer behaviour. Their algorithm-driven dynamic pricing approach has played a crucial role in staying competitive and maximising revenue.

Netflix is another noteworthy case. The streaming giant consistently experiments with its pricing plans to strike the perfect balance between subscription fees and meeting customer demand. Using customer data and predictive analytics, Netflix optimises its pricing to offer a compelling value proposition.

3. Place and omnichannel personalisation

Place, in the traditional 4Ps framework, also known as distribution, involves determining how and where customers can access the product or service: offline shops, webshops, apps, marketplaces, or such.

In the world of e-commerce, place and promotion are extremely interlinked. When talking about place personalisation, I would like to touch upon omnichannel personalisation as one of the ways to personalise "place." Here, an app or a website, and less commonly a physical store, delivers personalised interactions by using first-party data.

Omnichannel personalisation doesn't mean offering customers the same experience everywhere but a cohesive, seamlessly relevant buyer's journey across distribution and advertising channels.

On a hands-on level, omnichannel personalisation can include personalised: 

  • Product descriptions
  • Banners
  • Images
  • Videos
  • Sales copy
  • CTAs
  • Search results
  • Product recommendations
  • Lead generation forms
  • Email list segmentation with AI recommendations
  • Landing pages for your ad campaigns
  • Surveys to learn more about your audience
  • Chatbots to provide personalised support to customers 

Ninetailed.io, Relevize, Bannerflow, Clerk.io, Klevu, Clearbit, Coveo, Idomoo, Nosto, Dynamic Yield, Personyze, Proof, and many more providers help to facilitate omnichannel personalisation and boost engagement, conversion rates, and customer loyalty.

If web and app experience personalisation is common, expanding personalisation to offline places is an ample, often untapped opportunity. Store workers can use data and advanced analytics to give you personalised suggestions, and personal shoppers can use smart tools to do a better job. Plus, things like recognising your face, knowing where you are, and using body sensors might become more common. Sephora and Neiman Marcus are great examples of a brand that is exceptionally skilled at personalising customer experiences.

Neiman Marcus ensures that all its customers have a luxurious experience along the customer journey, whether on the website, in the store, or using the mobile app. On their website, they've made it easier to find the right size when you're shopping online. The website remembers if you keep looking for a specific size, like a shirt or shoe size. It even checks nearby physical stores and shows if they have that size. 

In-store, they have something cool called "Memory Mirrors". These mirrors can record you trying on clothes. You can send these videos to friends or family for advice or to your Neiman Marcus app to check later. It's like seeing how the clothes fit you, not a mannequin.

Neiman Marcus also has an app called "Snap. Find. Shop" that's like magic. If you see an outfit or accessory you like, just take a picture with the app. It will search Neiman Marcus to find something similar for you to buy. You can save it to use later.

4. Promotion personalisation, customer acquisition, and the cookieless world

Promotion in the 4Ps framework is linked to communication and advertising. Tailored promotions, recommendations, and marketing messages can significantly improve engagement. We've already partly covered promotion personalisation as part of omnichannel personalisation above, mostly powered by first-party data. 

In this section, I would like to talk a bit more about advertising personalisation in the cookieless world and the challenge of reaching new users with relevant messages as third-party cookie data comes to an end.

While cookies haven't yet phased out, behavioural targeting (and personalisation) is still in the game for a short time. Nevertheless, it is high time to realise that personalisation in advertising will change. Sad to admit, but advertising personalisation is not within the control or influence of midsize or large businesses; it is rather within the zone of concern. 

When third-party cookies disappear, large ad platforms will develop more ways to provide personalised advertising. Google has already introduced FLoC as part of its Privacy Sandbox project. FLoC helps remove third-party cookies from advertising by grouping users who share similar interests into cohorts. Other publishers implement Unified ID 2.0, which leverages encrypted email and phone number data to provide a privacy-conscious, secure, and accurate identity standard for the entire digital advertising ecosystem.

Advertising personalisation in the cookieless world could also be facilitated by implementing:

  • Zero-party data-based targeting, using voluntarily provided data by a consumer to the company. 
  • Intent-based targeting, utilising contextual advertising with words chosen by advertisers, can show ads to people who care about words related to products or services. Some ad networks allow showing context-based ads without using third-party cookies. This means you can show the right content to people without needing their data for personalisation. With advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI), contextual ad targeting is becoming more sophisticated and precise. Although contextual advertising is helpful, it's important to use it in combination with other personalisation methods.

Conclusion

As a Martech consultant, I urge you to embrace personalisation across the 4Ps framework to generate more revenue and profit, but keep in mind: Personalisation is a marketing technique and should not conflict with the general business strategy.

Personalisation is a tool and requires data to be correctly and legally collected and interpreted. It comes with costs linked to data management and implementation. Regardless of whether a company is building a solution or buying a personalisation technology, one should remember that technology is an enabler and nothing more.

Providing personalised customer experiences across the 4Ps requires companies to strike a balance between using data to enhance the experience and avoiding being "creepy”.

Columbia Road can help make the biggest impact on revenues and profitability by a tailored approach to personalisation program development implementation. 

The Data Handbook

How to use data to improve your customer journey and get better business outcomes in digital sales. Interviews, use cases, and deep-dives.

Get the book