7 Strategies For B2B Marketing Personalisation

No comments yet

Personalisation and marketing automation are now proven strategies in digital B2C marketing for increasing conversion rates and revenues. For example eConsultancy estimate that the suggest feature on Amazon.com generates an additional 10 to 30 per cent in revenues. Given the huge sales on Amazon this generates many millions of dollars for the company.

Amazon’s “Customers who bought this item also bought”

B2B websites have been significantly slower to adopt personalisation. However, according to research by Forrester. B2B buyers are increasing their use of digital channels to research and complete transactions. Whilst still using more conventional channels during the customer journey.

Furthermore, their experiences with B2C online sites has raised expectations of B2B sites. Buyers now demand much higher levels of service and personalisation during the user entire journey.

How do B2B sites up their game and begin to deliver on personalisation? In this post I will cover the following seven topics.

B2B Personalisation:

  • Personalisation vs customization – How are they different?
  • Enable B2B users with personalisation – Why it’s not all about selling in B2B?
  • Getting data for B2B personalisation – What and how to get it?
  • Choose a personalisation platform – What are your options?
  • Segmenting your B2B customers – What strategies should you use?
  • Implementing personalisation – Strategies for creating a personalised experience!
  • When personalisation goes wrong – Why does personalisation sometimes go wrong?

1. Personalisation or customisation?

Customisation

These two terms are often confused with each other. Customisation refers to where prospects are offered a distinct choice, perhaps to indicate their sector or occupation. This allows the website to respond to this selection by making the experience more relevant to the user’s needs.

Customisation allows users to remain in control and select exactly what they want. The downside is that visitors don’t always know what they need or how a solution could improve the efficiency of their business. But customisation definitely has a place for many B2B websites. Especially for first time visitors where you don’t know anything about their needs or the nature of their organisation.

B2B E-commerce site using pop-up to enable customisation

Personalisation

Personalisation on the other hand refers to automatically serving content that is relevant to the individual visitor based upon data captured on the user. The advantage of personalisation is that it does need any additional effort from the user because your automation platform does the hard work.

However, this does mean you are reliant on the data and analytics of your marketing platform to identify or infer each user’s needs. In some cases this can feel a bit creepy as visitors find that the website is too good at anticipating their preferences. We’ll deal with this issue later after we examine how to personalise the B2B customer experience.

2. A complex user journey:

B2B marketers have to target high-level decision-makers or a group of individuals and face a significantly longer business cycle than most B2C marketers. The B2B user journey is often has many more touch points and a complexity that appears to make personalisation more difficult to achieve.

Strategies for B2B Personalisation

B2B personalisation has the potential to significantly increase website revenues

The solution to this challenge is to create a user experience that enables visitors rather than sells to them. The B2B buyer will normally have to consult colleagues and is often heavily influenced by peers outside their organisation. To facilitate this process it’s important to create a user experience that automatically adapts to the behaviour and interests of the visitor rather than trying to push them towards a purchase.

3. Getting data for B2B personalisation:

Before you spend time and money on your data you should first identify what will be genuinely valued by your customer and what you need to know to achieve that. Start with the end in mind rather than the other way around.

The difficulty faced by B2B marketers is combining data about an individual visitor with information held about their organization in a meaningful and actionable way. Furthermore, it’s important to combine all types of data (e.g. quantitative or qualitative) and channels (e.g. offline and online) to capture information on all touch points to build a picture of the prospect’s behaviour and interests.

Once you have the data you need you can begin to systematically generate inferences from your visitor and customer database to identify insights and create opportunities to personalise the customer experience.

Data

Sometimes expertise is underrated and companies recruit junior people with little experience. Saving money on personnel is counterproductive. A good data analyst or data scientist understands the capabilities of analytical and marketing automation tools. They know how to get the most value from your data solutions and can identify and develop new opportunities for personalisation without having to be given specific directions from marketing management.

However, obtaining data for personalization is often simpler than you think. Most web analytics software for instance captures a wealth of data on the characteristics and behaviour of your visitors. Google Analytics for example can provide you with data to personalize as follows:

New vs returning visitors

  • Someone who has visited your site more than once in the last 24 hours is likely to different motivations and concerns than someone who visits your site for the first time. Even a simple welcome back message can acknowledge that you recognize their high level of interest in your product or service.

Landing page type

  • Use your knowledge of the type of page a visitor lands on when they arrive on your site to personalize their experience by ensuring consistent messaging and content is displayed throughout the user journey. Don’t waste money on building dedicated landing pages if you are not going to use that knowledge to personalize subsequent screens with relevant content.

Day of week / Time of day

  • Does a visitor who browses your site on a Friday afternoon have different intentions and motivations than someone who is browsing your site on Monday morning? What about weekend traffic – some businesses don’t close on a Friday afternoon – shouldn’t your content reflect this demand at the weekend?

Conversion funnels

  • Where do visitors drop out of your funnel most often? Why not examine how you can personalize these stages in the process to improve engagement and reduce drop-out rates.

Page load time

  • Why not acknowledge when your site is taking longer than normal to load by personalizing messaging or content to win visitors back. People like it when companies say sorry.

IP address

  • Most medium to large companies have a unique IP address and so you should be able to identify key organisations to target from their IP address.

4. Choosing a personalisation platform:

According to a study by Gleanster for Act-On. 83 per cent of B2B marketers believe fragmented marketing platforms and systems prevent the implementation of marketing automation. This suggests that many B2B marketers either don’t have personalisation software or have not been able to fully integrate it with existing systems.

Begin By Deciding What Your Goal Is

  • Increase the conversion rate of lead generation efforts?
  • Improve cross-sales?
  • Encourage repeat purchases or reduce cart abandonment?

You can then set KPIs to monitor progress and begin considering personalization techniques. Start to look at what software is needed. There are generally two types of software available, A/B & multivariate testing solutions that offer personalisation as a feature and dedicated marketing automation software. They include: Adobe Marketing Cloud, Google Optimize 360, Optimizely, Oracle and Quibit. If you already have an A/B or multivariate testing tool check out its capabilities for personalisation as you may find this can meet some of your needs.

Top Personalisation Software Companies:

  • Act-On
  • Acquia
  • Baynote
  • BevyUp
  • Boomtown
  • Certona
  • Dynamic Yield
  • Edgeverve
  • Evergage
  • Flytxt
  • IgnitionOne
  • Magnetic
  • nectarOM
  • Peerius
  • Real-Time
  • Syntasa
  • Strands

A key consideration here is finding a platform that integrates with your legacy systems and CRM solution in particular. Personalisation requires real-time access to data across all customer touch points. It may be necessary to establish a network of technology partners who have the experience and knowledge to plug gaps where they exist in your internal capabilities.

5. Segmenting your B2B customers:

A popular strategy for creating a customer experience is the buyer persona. This involves building a profile of important user segments: demographic and firmographic data. Such as job title, function, management level, budgetary responsibility, and industry sector.

Buyer persona template online tools

Creating a buyer persona for each key customer segment using data and research allows organizations to improve their understanding of customers and prospects. It enables them to construct a more personalized and targeted customer experience.

Other Strategies:

  1. Segment specific – Use industry vertical or customer segment criteria.
  2. Stage specific – Apply personalization according to stage in the buying process.
  3. Account specific – Use details of the prospect organization to tailor the experience.
  4. Lead specific – Tailor according to details of the individual lead.

Personalize the User Experience:

  • IP address for large organisations to target individual companies.
  • Geographical data such as city, region, country or seasonal factors.
  • Behaviour on device (desktop, mobile and tablet).
  • Demographics such as gender, age or cultural background.
  • First party data – information that you have captured yourself and your customers are aware you hold.
  • Third party data – information from CRM or social media and other third-party sources that users may not be aware you hold about them.

The important point to consider here is to use experimentation to identify what works and what doesn’t for your prospects. Best practice is only a guide and should not be taken as gospel.

6. Implementing B2B personalisation:

Now that you have your detailed buyer personas you can identify key characteristics or behaviours that allow you to allocate a visitor to a specific persona. Use these criteria to segment your email list into, smaller, more targeted lists. This will allow you to deliver personalised email campaigns based upon important drivers of behaviour: job title and management level.

Image of personalised email from 47 Links

Using your buyer personas to classify web visitors should also allow you to deliver a highly personalised web customer experience to replace generic and static web content. As Karl Wirth, CEO of Evergage, points out there are four core principles of user experience which marketers need to consider. These are remember me, understand me, help me and surprise/delight me.

Remember

  • Retaining and utilising information about the user’s behaviour or profile to deliver a personalised customer experience. This means acknowledging returning visitors and ensuring the user experience reflects past behaviour and previously captured on the prospect.

Understand

  • Recognising returning visitors and applying the knowledge the organisation holds on customers to deliver content based upon their known interests and needs.

Help

  • Making it easy and enjoyable for visitors to achieve their goals. This involves monitoring user browsing behaviour and purchasing history to provide relevant recommendations and directing customers to useful information or recently viewed items. Don’t make it difficult for users to find what they are looking for.

Surprise and Delight

  • Going that little extra mile to acknowledge good customers and inform visitors of relevant and available offers can go a long way to make users feel valued.

Buyer personas also make it easier for you to identify potential prospects in the social sphere. Listening to conversations people have on social media and having one-to-one dialogue with prospects can allow you to better understand their problems and needs so that you can tailor content accordingly.

Engaging with prospects on social media demonstrates to your audience that you value their input and help create the ultimate user experience.

7. When personalisation goes wrong:

Predicting behaviours is never easy and so it is inevitable that sometimes you will get it wrong. Poor personalisation leads to a poor user experience. You can minimize this by doing extensive qualitative and quantitative research. Understand your audience and ensure your buyer personas are based upon real customer segments.

Personalisation should only be used when it’s helpful and has a clear benefit for the user. It should be intuitive, useful and create a natural user experience. It should not feel “creepy” or like Big Brother is watching you.

It’s important to be open and transparent with customers about using data to personalise the user experience to manage expectations and reduce the chance that it becomes “creepy”. Personalisation can become problematic when organisations rely too heavily on inferred or purchased data that has not been freely given by users. When customers voluntarily give information or confirm the accuracy of data this reduces the likelihood that it will be perceived as “creepy” when it’s used to predict customer needs.

Example

This example below of personalisation from Evergage allows the visitor to see why content has been recommended to them. This level of transparency helps to avoid personalisation becoming “creepy”.

There also needs to be a value exchange for all concerned and so it is important to set out what personalisation means for different buyer personas or segments to fully understand the benefits for all parties. Holding user data is a privilege and so it is essential to set high standards for how it is stored and used.

The rise of big data has led to a culture of hoarding data that companies don’t use and visitors don’t know they have. Like any asset data deteriorates over time and so it is important to regularly review and cleanse data to ensure it still usable. To avoid storing up problems for the future you should ask two questions every time you capture a new item of data:

  1. What will the customer’s attitude be towards us holding this data?
  2. What do we want the customer to do?

Unless you can clearly answer these questions it is best not to use the information. In addition, ensure your data base architecture enables you to identify which data was given freely and which was bought from third-parties.

Don’t forget to build in self-service alternatives by designing your information architecture to enable users to easily locate what they are looking for without having to rely on personalized content. This helps to prevent problems occurring when your personalization goes wrong.

Conclusion:

Personalisation should begin and end with what’s best for the customer. Set clear goals for what you want to achieve and invest in both qualitative and quantitative research to get a much deep understanding of what motivates your prospects. Create strong buyer personas based upon evidence to help guide your strategy.

Avoid hoarding data for the sake of it. Always have a clear view of your objective and seek out data that allows you to achieve that goal. Give priority to data that has been given freely by visitors and don’t be over-reliant on making inferences from third-party data.

The challenges of B2B personalization reinforce the need to have the right tools for the job. Even the best research and insight is of little value unless you have a platform that is capable of extremely fast delivery of content and is scalable.

There will always be instances when personalisation doesn’t work and so build in self-service options to enable visitors to find what they are looking for. Avoid over-reliance on third-party data and be open with your visitors about how data is used.

However, when used effectively B2B personalisation can be a powerful strategy for improving the customer experience and for increasing conversions. Just because the B2B decision making process is more complex this should not be a barrier to using personalisation to generate more revenues from your digital user experience.

The Myth of The Average User

1 comment

Averages are everywhere in digital marketing. Mobile designers use average thumb size to determine button height and project teams often base decisions on the average user. Many metrics are also based on averages such as click-through rates, open rate, conversion rate and average basket value. Whether we like it or not most websites are designed for the average user. But is there really such a thing as an average customer or visitor?

Should we use averages for design purposes?

Well, back in the 1940’s the US air force had a serious problem. For some unknown reason pilots were frequently losing control and crashing their air craft. This was of course a period of tremendous change with the advent of the jet engine. Air craft were getting much faster and more complicated.

Initially pilot error was blamed as planes seldom suffered from mechanical breakdown. But attention soon turned to the cockpit design. This was based upon the average physical dimensions of hundreds of male pilots measured in 1926. Was it possible that the average dimensions of pilots had got bigger over the past twenty odd years?

Data informed decision-making:

In 1950 they decided to find out. Researchers at Wright Air Force Base in Ohio measured over 4,000 pilots on 140 dimensions of size, including average torso length, arm length, crotch height and even thumb length. Almost everyone thought the new measurements would result in a better designed cockpit that would reduce the number of non-combat accidents.

However, a 23 year-old scientist, Lt. Gilbert Daniels, who had recently joined the Aero Medical Laboratory from college had a different theory. He had studied physical anthropology at college. Daniel’s thesis had involved measuring the shapes of 250 male Harvard students’ hands.

Although the students were all from similar ethnic and socio-cultural backgrounds, he noted that their hands were very different in size and shape. Further, when he calculated the average hand size he found that it did not match any individual’s measurements.

“When I left Harvard, it was clear to me that if you wanted to design something for an individual human being, the average was completely useless.” – Lt Gilbert Daniels

To prove whether or not he was right, Daniels selected ten physical dimensions that he thought would be most important for cockpit design. Using the data from the 4,063 pilots who had been measured, Daniels defined someone as average if their measurements fell within the middle 30% of the range for each dimension.

He then compared each individual pilot to the average he had calculated. Most of his colleagues expected the vast majority of pilots to be within the average range for over half the dimensions. But in fact Daniels analysis discovered none of the 4,063 pilots measured managed to fit within the average range of all ten dimensions. Even when he selected only three dimensions fewer than 3.5% of pilots were within the average size for all three dimensions.

Implications for digital marketing:

Daniel’s concluded that any system that is designed around the average person is doomed to fail. There is no such thing as an average user and so we need to stop creating users or personas based upon averages.

This creates a problem for website designers and optimiser because websites are normally designed for the average user. Most websites display identical content for all visitors and yet people have different intentions and goals they wish to meet. Treating everyone the same based upon some illusionary average person is highly toxic and dangerous when it comes to design and conversion rate optimisation.

How do we individualise the user experience:

If one hundred users go to the Amazon website they would each see a different version of the Amazon homepage. This is because Amazon understands the benefit of adjusting the customer experience in according with the user’s past behaviour and intent.

Amazon uses real-time content personalisation and behavioural targeting to serve a version of their site that responds to each visitor’s unique needs. This generates huge benefits for the likes of Amazon because visitors are much more responsive to a website that adjusts to their intent and interests than a generic site that does not respond to their individual needs.

Personalisation can take many forms, but the main criteria often used include demographics (e.g. gender or age), purchase history, device, media consumption, source of traffic, service history, browser, engagement and psychographics.

When I mention personalisation to web developers they often tell me that it’s “difficult” or “complex” to target content using such characteristics. This might be the case if you rely on developers to build content, but if you have an enterprise web analytics platform or an A/B testing solution it can be relatively straightforward to set up and test personalisation criteria.

With the introduction of artificial intelligence (AI) based personalisation tools there is scope for even greater sophistication. Companies that invest in AI are likely to benefit from first mover advantage because the technology lends it so well to personalisation. Don’t be left behind, start investing now as Amazon and Booking.com won’t wait for their competitors to catch on to the potential benefits of using AI for personalisation.

Personas:

Many organisations like to use buyer personas to help their teams visualise real customers. However, if these are based upon average users they will again be potentially highly misleading. Ensure your buyer personas are based upon real customer segments using research and analytics to guide you. Although personas do have their critics, they can be useful if organisations go through an evidence based process to create relevant customer personas.

What about analytics?

When it comes to tracking digital performance many organisations still rely on measuring averages. But just as averages are dangerous when designing a website, they are also meaningless and potentially highly misleading when it comes to measuring performance of a website. Let’s take the average conversion rate that many companies monitor on a daily basis.

1. Not all visitors are able to buy: 

When I was asked to set up conversion reporting for an online gaming brand, I noticed their web analytics were tracking all visitors, including from countries that were prevented from signing up. No one had thought to set up filters to exclude visitors from outside the company’s business area, and so the conversion rate included many visitors who were unable to sign up.

BJ Fogg’s behavioural model point’s out that users will only complete a task if they have both the motivation and ability to complete a conversion goal. In addition, there also needs to be a trigger to nudge the user towards the goal. If any of these criteria are lacking a user will not convert.

When reviewing a web analytics report consider if these criteria are present. If possible remove those users where they clearly lack at least one of the criteria. For example if there is no prominent call to action on the page for an individual customer segment (e.g. logged in users) exclude these visitors from your analysis.

Image of BJ Fogg's behavioural change model

Image source: BJ Fogg

2. Users access your site in different ways:

Your conversion rate is highly likely to vary significantly according to how visitors access your site. The type of device used often reflects different intent and behaviour. Unless you analyse your conversion rate by device and browser you will probably be missing large variations in your key metrics that may provide valuable insights to help improve sales or lead conversion.

3. Source of traffic matters:

Similarly the source of traffic often has a massive impact on conversion rates and it is fairly common for the average conversion rate to plummet if you pump lots of money into a new untested source. Affiliates and paid search (PPC) can promise large amounts of extra traffic to a site, but the intent of these visitors can sometimes be very poor.

A TV campaign can also boost traffic volume significantly, but again the intent of such visitors will be different from existing traffic sources. This makes it is essential to break down conversion rates by source of traffic to understand performance at a more granular level.

4. New and returning visitors:

In one company I worked for the managers noticed that a majority of visitors were returning visitors and assumed that many of these would be existing customers. They were concerned that including returning visitors in reporting was reducing their conversion rate as customers couldn’t sign-up more than once. So they decided to exclude returning visitors from their calculation of the conversion rate.

But as I pointed out to them when I became responsible for the brand, returning visitors normally convert at a higher rate than new visitors. This means that you should look at new and returning visitor conversion rates separately, but use new visitor conversion as a guide for paid campaigns. When I looked at the number of returning visitors to the site it was also clear that relatively few were existing customers and so they were not having a significant impact on the conversion rate.

5. Visitors are at different stages of buying process:

Most websites have a mixture of informational content and transactional or lead generation content. This reflects visitor intent and that visitors are at different stages of the buying process.

Not everyone is ready to buy when they arrive on your site and so it is necessary to create custom segments in your analytics to allocate people to an appropriate group. As a result you should set appropriate success metrics for customers at different stages of the buying process and not expect your overall conversion rate to be identical for all visitor segments.

Conclusion:

Averages are a tidy way of dealing with statistics, but as Daniel’s identified over half a century ago, they are meaningless and potentially fatal when designing systems or interfaces for people to use. It’s time we stopped designing websites for average users and employed personalisation and behavioural targeting to better meet customer needs.

We shouldn’t be a surprised that according to Millward Brown Digital, Amazon Prime converts around 74% of the time compared to an e-commerce average of 3.1%. Even non-Prime Amazon converts around 13% of the time. This is mainly because Amazon is so good at testing and personalising their site to be responsive to individual customer needs.

Amazon runs literally thousands of A/B and multivariate tests a day to achieve this level of sophistication. This is because to find high impact experiments you have to try a lot of things. Most average retailers run a few hundred tests a year.

As a result companies such as Amazon, Netflix and Booking.com also use highly segmented web analytics reports to explore user behaviour. They don’t rely on average conversion rates because they hide real insights.