February 11, 2021

The Fourth Law of Customer Retention

Cross-Channel Messages are the Attraction Forces That Influence Organic Product, Service Discovery & Repeat Customer Visits

In the first two laws of customer retention, we focused on determining the probability of retaining a customer and the retention pulse of an experience to enable us to easily monitor its health. In the third law of customer retention, we explored the influence that retained customers have on the organic growth of new customers and a method to measure the level of its influence.

In the fourth law of customer retention, we will expand our focus to encompass the effects of a business’s cross-channel engagement messages on the level of retention seen inside a customer experience. Whether the effects turn out to be positive or negative is determined by the offer presented to said customer and the path the message sends the person down. If a business sends out unengaging or irrelevant offers to their customer base, we could expect to see a low number of visits generated by these customers from the messages received. Further, if the experience the person has to go through after clicking an engagement message is cumbersome and friction-filled, you might also see a low percentage of customers taking action.

Both the visit (or session in the context of a digital product experience) and any value actions (ad view, transaction or productivity workflow in the context of a digital product experience) are important aspects to monitor when determining the effectiveness of cross-channel messaging campaigns.

In order to do this, we will need to keep track of all messages sent and the corresponding activity and actions that they generate. As you might know, this is only possible to track for those who have revealed themselves to a business by signing up for a free account or by previously making a purchase, for example. In practice, this typically requires special analytics tools to do this at scale. Let’s assume for now we do have this data available to us for analysis purposes.

If we began to look at slices of this data over defined time periods (daily, weekly or monthly for example – or even longer for certain experiences that have infrequent customer visits by nature such as car shopping), we can start to see how enticing the offers are in your cross-channel messages. By gathering this information, it will offer us the insight we need to make the inferences and conclusions that can help drive our decision making in the future. Let’s break this down.

Specifically, let’s compare the ratio of visits and value actions (ad view, transaction or productivity workflow) that started as a direct response to an engagement message (more than likely from a link inside an email, SMS, push or QR code) to the total messages viewed from the same body of people over the same time period. The attraction force present inside a business’s cross-channel engagement messaging can be described as follows:

Also, remember to exclude visits and value actions that might have been influenced by an engagement message, but not as a direct response from a message. This will help to remove subjectivity from the analysis. Clearly this will make it harder to score “great” on the evaluation spectrum. But don’t be too concerned with getting a perfect score of one as it is highly unlikely it will occur. Further, average and good on this spectrum will be heavily dependent on industry and customer segments. Social media apps might expect to see scores greater than 0.5 while a QSR app might be lucky to get north of 0.25 on average.

However, regardless of what industry you are in, this can serve as a solid and consistent benchmark to monitor the health of your cross-channel engagement messages and evaluate if a change you made was positive or negative.

Let’s assume for a minute we are managing a digital product in the social media space, which on average sees attraction forces north of 0.5 and currently our factors are at the following levels:

Now, let’s assume that we took action and made changes to our cross-channel engagement messaging and the resulting factors are now as follows:

It becomes quite clear how one can use these factors to monitor and improve cross-channel engagement messaging to drive higher retention outcomes. Visits are the leading indicator to a potential repeat customer and value actions are the retention gateway in the customer retention lifecycle customers must go through in order to become a retained customer per the second law of customer retention.

In summary, cross-channel engagement messages are the attractive forces that drive repeat visits and value actions within a business’s customer experience. This influence can be measured by looking at the number of visits and value actions that were started as a direct response to a received message as compared to the total number of messages viewed by the same body of customers. The two resulting factors will be a value that falls between zero and one; with one being the highest score and zero being the lowest. Finally, teams can use these factors and industry averages to determine where they stand on the spectrum and how any shifts can increase or decrease retention levels.

February 8, 2021

12 Ways For Tech Companies Using Consumers’ Data To Earn Their Trust

In the remote-first era of Covid-19, the potential for and frequency of cyberattacks has increased significantly. With data breaches regularly hitting the headlines, many consumers are wary of giving tech companies access to their personal data.

So how can a tech brand anticipate this and assuage the concerns of consumers who are reluctant to share personal information? Below, 12 members of Forbes Technology Council shared tips for companies that want to build trust with consumers when it comes to using their personal data.

1. Invest In Blockchain Technology

I love the idea of a blockchain-based profile that puts the control around how their personal data can be consumed completely in the hands of the user. It would be even better if such a profile could actually help the user monetize sharing of their data. - Michael FultonExpedient

2. Adopt A ‘Privacy By Design’ Approach

We must ensure that the collection and use of private data are intentional and explicitly communicated to consumers. If a personal data element is not required, then it is imperative that we do not collect it in the first place. Adopting a “privacy by design” approach to process and application development is a key method for maintaining compliance and building confidence with consumers. - David StapletonCyberGRX

3. Share Case Studies Of Brands That Trust You

Building trust is hard in general, but especially in the current, all-remote situation. One of the most effective ways to convince customers is to show them case studies of companies that already trusted you, especially well-known brands within a similar niche. - Robert KrajewskiIdeamotive

4. Apply Transparent Data Use Standards

Consumers view tech companies as independent operators, each with their own business agenda for personal data. Tech companies can create trust by creating joint, transparent standards that they apply to the use and management of personal data. With Big Tech signing on to a universal set of data use standards, consumers can better understand how their data is used. - Micheal Goodwin[email protected]

5. Show That You Value Personal Data

Choose who to do business with based on how seriously they take security. That includes providing two-factor authentication and guarantees of refunds for any losses they cause. The best way to convey your serious attitude with personal data is to show that you value it. For example, give the consumer something of value in return for their data instead of just asking, “Can I have it because I benefit?” - Mike LloydRedSeal

6. Work With Policymakers On Data Governance

Industry and government need to work together to create a clear, enforceable and definitive framework for data governance. With businesses all using the same playbook, consumers will have their faith restored in the use of data and how it is regulated. Not all data, nor its uses, are the same, so consumers need to ensure their data is used in a responsible and authorized way. - Sam AmraniOlvin

7. Have A Third Party Audit Your Security

First, be sure to collect only the data that’s needed and be really transparent about how and why you are doing it. After that, you can work with a third party to periodically audit your site’s security and fix any vulnerabilities, which will likely show up as your software develops. Reiterate your commitment to cybersecurity in as many user touchpoints as you can, and you’ll be well on your way! - Nacho De MarcoBairesDev

8. Obtain Consent Before Collecting Data

Consent is critical. By relying on consent-based data collection methods, companies can collect data with the consumer’s explicit permission. Consent management databases can also help to build trust by transparently showing what consumer data is being used for and giving consumers the continuous option to remove consent at any time. - Sanjoy MalikUrjanet

9. Adopt Policies That Favor Consumer Privacy Concerns

With its General Data Protection Regulation, the European Union is showing that consumers do actually value their privacy. So adopting policies that favor customer privacy concerns over extra profit from selling their data—and being clear about that choice—will help build trust. Be transparent. State your privacy policy clearly and in a way that the average, non-technical user can understand, and then hold to your own policies. - Saryu NayyarGurucul

10. Show Social Proof On-Site

We build trust with our customers by showing social proof on-site. When your audience can see verified trust seals and reviews from countless customers, they are much more likely to trust your business. You can even grab testimonials from high-profile clients and put them on your homepage or landing pages to show that well-known people trust your brand. - Thomas GriffinOptinMonster

11. Establish Transparency Through Clear Communication

To build trust with your customers, you must establish transparency. Are you deploying a new software update? Clearly explain why and what the new features are to your users in plain language. Do new data regulations affect how you collect and use customer data? Let your clients know right away. It sounds simple, but transparency is the foundation of trust. - Marc FischerDogtown Media LLC

12. Reiterate Your Message Often

Reiterate your message as often as necessary. If your customers are concerned about their personal data within your system, every touchpoint is an opportunity to reiterate your commitment: every email, every press release and every blog post. Mention one specific way you protect their data at a time, and focus on your overall commitment to security. - Luke WallaceBottle Rocket

This article was published on Forbes.com

December 22, 2020

The Third Law of Customer Retention

Retained Customers Are The Growth Engine Behind Organic Customer Growth

In The First Law of Customer Retention (previously published here), we defined retention and provided an equation which helps determine the probability of whether or not a customer will be retained. In The Second Law of Customer Retention (previously published here), we showed that customer retention is not a single moment in time, but rather constantly changing states triggered by the actions people take in response to digital and physical experiences over the course of their relationship with a business.

As a reminder, we learned in the Second Law that the final stage of The Customer Retention Lifecycle is a repeat transaction, ad view, or workflow completion which moves a customer into a retention state of “power”. These customers have proven their loyalty to your brand as they continue to come back time and time again and form the basis of your “long-term retained customer base”. The reason these customers are so valuable is about more than the return on the effort it took to get them there. These same customers are also the growth engine behind your organic growth because these customers are highly likely to also be advocates for your business, telling other potential customers about the positive experience they have had.

This effect is more commonly understood as “word-of-mouth” acquisition and is notoriously hard to measure and influence as most of this social sharing occurs offline, leaving us blind to the majority of these interactions(1). In order to measure this effect consistently across your business and customer experience, we must look to an abstraction that we can measure which gives us an indicator as to how we are performing here and if any changes in the business or customer experience result in positive or negative growth.

So, what is this measurable abstraction that gives us this insight and what is the justification for its validity? We mentioned earlier that the driving force behind new organic customer growth are long-term retained customers who talk about their positive experience with other people. If we assume that on average a retained customer talks about their experience with family and friends at a consistent rate, we can define a simple relationship between the number of organic customers that are added at each defined interval. This assumption and relationship is defined as follows:

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Word-of-Mouth Coefficient by Yousuf Bhaijee at Reforge (1)

What this equation is telling us is the number of new organic customers that are added to the businesses’ ecosystem for each active customer currently in the ecosystem over the time interval defined (day, week, month, etc.). It is a ratio that can be used to predict the number of customers acquired through word-of-mouth acquisition and can be used to measure the impact of efforts to improve it. (1)

Let’s consider a blockchain and cryptocurrency services company as an example of how this equation is used in practice. Let’s assume during a single week this business has 150k in active returning, paid and non-organic customers in the product experience. Further, let’s assume that during this same week there were 25k new organic customers seen in the business’s product experience.

The Word-of-Mouth Coefficient for this given week for this business would be as follows:

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Word-of-Mouth Coefficient Example Calculation

This metric taken over time can start to paint a picture that trends and forecasts can be developed around. Further, this baseline can be used in parallel with graph annotations which detail changes in the business and/or product experience to determine if said change had a positive or negative impact.

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Word-of-Mouth Coefficient Measurement Over Time With Annotations

From the graph, we can see an example of the Word-of-Mouth Coefficient expanded over time with two important annotations marking two events that impacted the customer experience during the same time intervals we are interested in. Notice that the Word-of-Mouth Coefficient increases from 0.18 to 0.19 between week 3 and 4. We can conclude that the introduction of this feature had a positive impact on increasing the word-of-mouth effect and should be permanent. On the other hand, we see that the Word-of-Mouth Coefficient drops from 0.2 to 0.185 between weeks 5 and 6 which is when a new national campaign was launched. From this we can conclude this campaign had a negative impact on the customer experience and should be rolled back and evaluated for future interactions.

In summary, the major force behind new organic customer growth are your retained customers who are willing to serve as advocates for your business (mainly among family and friends). “Word-of-mouth acquisition” is real and can be measured by comparing a few existing metrics. This ratio offers busineses a new means to forecast organic customer growth and to measure the impact of business and product changes for iteration and improvement purposes.

December 10, 2020

Navigating the Product Analytics Maturity Model in the First-Party Data Era

First things first, let’s level set. If you are a product manager that is feeling increased pressure from your business to use your first-party data to drive digital product growth, you are not alone. It is common knowledge among product professionals that product analytics is critical to driving growth, but the bar to meet the increased expectations from both your business and customers is only getting higher as we move into 2021. We believe this is being magnified largely due to three factors:

  1. The prolonged pandemic has caused most businesses to see digital products as the only means to engage with a large portion of their customer base.
  2. Apple’s shift away from the IDFA will remove end-to-end customer journey visibility for many advertising, marketing, and product teams, thus resulting in the need for more dependable insights.
  3. Third-party cookies continue to crumble after Google said it will begin phasing them out from Chrome browsers.

In response to this pressure, all eyes are turning to customer and employee first-party data–the data your product team or wider business has collected directly from your customers, site visitors, and social media followers as they interact with your business.

Because the party collecting the data does so first-hand, without additional layers like Apple’s advertising identifier, first-party data is typically considered more dependable. This data can hold important answers teams need to continue creating and evolving the quality digital products and services their customers expect. Whether you are taking your first step into digital as a business, like T.J. Maxx for example, or if you are a mature, product-led business that makes data-based decisions like Airbnb, it helps to have a clear framework to help navigate this journey and increase your digital maturity by better leveraging first-party data along the way.

 Product Analytics Maturity Model from Mixpanel's Guide to "Advance Your Product Analytics Strategy" source

Product Analytics Maturity

As Mixpanel outlines in their Advance Your Product Analytics Strategy guide, product analytics maturity refers to where a company stands in the product analytics lifecycle. At its core, product analytics maturity is made up of four pillars: data collection, depth of analysis, collaboration, and product metrics. Your product analytics maturity determines your capability within each of these areas, and ultimately how the business is relying on data to make product decisions.

If you are unsure of where your product currently stands, you can take this online quiz that comes along with the Advance Your Product Analytics Strategy guide – here.

Asking the Right Questions

The questions that your team(s) ask throughout your product development lifecycle (strategy, design, build and grow), will influence future decisions and the future of your product. Ultimately, the depth of insights that you can ask of your tool—or that you’re asking of your own product—is the primary driver of maturity.

Throughout my interactions with various product teams, understanding how to figure out the right questions to ask and how these questions evolve over time is one of the biggest challenges I see product managers come up against.

Here are a few best practices I keep at my side when going into each new business endeavor:

  1. Be extremely specific with your questions. The more specific it is, the more valuable it will be. Instead of asking “How can we raise revenue?”, you should instead ask “Which new feature in the last six months drove the highest ROI and how can we replicate its success?” (source)
  2. Try it out yourself. Put yourself into the shoes of your customers and your competitors’ customers by regularly using your product and your competitors’ product. These regular “secret shopper experiences” are a gold mine for crafting impactful questions that drive immediate growth. Be sure to write down questions and pain points that come to mind in real-time as you go through the various product experiences.
  3. Industry and product type matters. Get a list of common questions asked by industry through your product analytics provider, digital agency, or by searching online. As stated above, the best questions are specific, and this specificity applies to industries as well. A transactional-based product in the airline industry will require very different questions to be asked than an attention-based app in the media and entertainment industry, for example.
  4. Ask your colleagues for help. Look to adjacent organizations in your business and ask them what their key performance indicators are and what questions keep them up at night. Not only is this a good way to expand the questions you are asking to encompass the goals of your wider business, but it is also an easy way to start a dialogue in a siloed work environment while addressing a common goal.
  5. Keep first-party data in mind. Consider whether or not data from 3rd party providers (ad networks for example) will be needed to answer certain questions. If you can get to an insight without relying on a 3rd party provider, do it. Further, if there are existing questions you can answer with first-party data, do it. This will save you from the headache when Apple follows through on this planned change in January. 

From our perspective, there isn’t a magic formula for generating these questions. It is mainly a skill that is built based on practice and experience similar to any other skill or sport you strive to be great at. I find keeping a clear list of current and past questions along with performance data to validate or invalidate impact is an easy way to reach the most relevant questions for your business.

In summary, as we move into 2021, your ability to grow and respond to changes your product encounters in the market will become increasingly more dependent on your company’s current level of product analytics maturity. Using a product analytics maturity framework to baseline your current level and to guide your product to more advanced levels is a critical component often observed in high-growth product teams. Further, the questions that you and your product team ask will drive the majority of your focus and should be created with a set of best practices in mind. If you want to get started understanding and then improving your product analytics maturity today, here are a few recommended next steps:

  1. Identify what your current product analytics maturity level is.
  2. Consider how third-party vs. first-party data factors into that maturity level.
  3. Plot your course to advancing your maturity considering any necessary shifts you should make in terms of the data you rely on to make decisions and the tools in your stack.

About the Author

Tim is the lead of Bottle Rocket’s growth practice and an active thought leader on digital product growth in the marketplace.

Sources

October 12, 2020

The Second Law of Customer Retention

Retention is not a Single Moment in Time, but Rather a Series of States Determined by Actions in the Customer Retention Lifecycle

In The First Law of Customer Retention (previously published here), we talked about how retention is a cornerstone to sustainable growth and a key driver of new organic customer growth within a business. We defined it as the volume of customers as a percentage of the total base who come back to an experience over time and continue to exert the effort and/or pay the cost to reach the value available in said experience.

Further, we provided an equation to determine whether or not a potential customer moving through a business’s customer experience will be retained based upon the ratio of value to cost and effort. This equation and the retention k-factor that it produces is powerful if used correctly, but is only a piece of a much larger retention puzzle.

This larger puzzle shows us that customer retention is not a single moment in time. Rather, it’s a series of states that are constantly changing based upon the actions people are taking in response to content and features they experience from a business.

These states are determined and governed by key action gateways that potential customers must go through to become a loyal customer who comes back time and time again.

The Customer Retention Lifecycle

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The Customer Retention Lifecycle

All potential customers will enter the customer retention lifecycle by becoming “aware” of a business through some piece of advertising or marketing content, or by word-of-mouth.

The first retention gateway that a potential customer goes through is the “identity” gateway, and potential customers make it through this gateway by creating an account or some other action where they give a business a way to identify who they are through an email or phone number for example.

It should be noted that businesses that do not require an account to be created or do not collect any personally identifiable information (aka — P.I.I.) are not able to effectively measure retention and thus this framework would not be 100% applicable. Further, many businesses, such as an e-commerce website for example, offer people the ability to checkout as a guest. While you don’t explicitly create an account, these brands often collect a personal identifier during the checkout process and append it to the purchase record and thus are able to measure retention states. You simply cannot measure customer retention without knowing who a person is in the context of your business.

After a potential customer establishes their identity with a business, they have two choices:

  1. Continue performing actions with the business
  2. Stop performing actions with the business

Below is a flow diagram showing the possible paths potential customers can take based upon these two different choices:

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The Customer Retention Lifecycle

Measuring Your Retention Pulse

In order to measure this lifecycle in your business and make it actionable, we need to view this retention lifecycle window over time and then draw conclusions from the trends. An example of how this can be visualized is shown below.

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The Customer Retention Lifecycle Over Time (Source — William Zhang, Amplitude)

From this visualization, we are able to see the flow of people between different retention states within a business’s customer experience. We can use this to measure the retention pulse of an experience which tells us the trajectory of retention at any given point in time (Source — William Zhang, Amplitude).

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The Customer Retention Pulse

If the Customer Retention Pulse (CRP) is less than one, that means the business is losing retained customers faster than adding new or resurrected ones and thus are in a state of retention decline for that given time period. Conversely, if this CRP is greater than one, that means the business is adding more retained customers than it is losing and thus are in a state of retention growth for that given time period.

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The Customer Retention Pulse Over Time

Shifting from Decline to Growth

If a business wants to shift from a state of decline to a state of growth, they need to add more value and/or decrease the effort and/or cost associated with reaching that value, as detailed in The First Law of Customer Retention here.

Insuring that potential and existing customers are always seeing more value than the effort and cost associated with acquiring that value, will give a business the best chance of keeping their CRP above one. Businesses that always maintain a CRP above one will always have a greater in-flow of new and resurrected customers than the out-flow of dormant users, insuring sustainable retention growth.

In summary, retention is not a single moment in time, but rather a series of states that are constantly changing based upon the actions people are taking in response to content and features they experience throughout their time with a business. These states are determined and governed by key action gateways and measured over time can be used to calculate the customer retention pulse of business to highlight if it is in a state of retention decline or growth for a given time period.

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