Consumer spending has changed drastically during the pandemic. We saw a boom in household supplies and food categories as quarantine lockdowns took effect, and the trend continued through 2020. Discretionary spending became a luxury for many as unemployment rates hit reached [nearly 15%](https://sgp.fas.org/crs/misc/R46554.pdf) in April 2020. With such drastic changes, businesses had to shift their tactics and appeal to a new type of audience: the at-home consumer, who is fueling the “homebody economy.”

As the name suggests, these are the consumers who are spending the majority of their time at home. They’re likely working remotely, and certainly making most of their purchases online. Because of this, e-commerce has taken center stage for retailers, forcing brands that hadn’t implemented strong consumer-facing digital strategies before 2020 to adapt quickly.

But this adaptation goes beyond setting up an e-commerce platform — many businesses have adjusted their digital strategies to understand the swaths of new customers coming in the door and reel in at-home consumers with more personalized ads. And now consumers expect better online experiences, with [68% saying](https://www.forbes.com/sites/forbestechcouncil/2020/12/17/how-the-homebody-economy-is-shaping-digital-customer-experience/?sh=5807ede562eb) they have higher expectations of brands' digital abilities since the start of the pandemic. So, how can brands deliver?

## Get to know your new audience

The adage “Know your audience” has never been more relevant, with somewhat unpredictable consumer trends changing the audiences brands may have thought were set in stone. As more consumers were forced to shop online because of the pandemic (e-commerce now makes up 16.1% of all U.S. sales, up from 11.8% in the first quarter of 2020) businesses everywhere encountered unexpected customers.

To provide the enjoyable customer experience people expect, marketers need to dig into their customer data to better understand who they’re serving. Personalization isn’t just about meeting the expectations of the customers you already know and love — you’ve got to capture (and retain) the interest of these new customers, too.

Leveraging responsibly sourced consumer data can help round out brands’ understanding of their customers and aid in personalization efforts that go beyond web-based activity. By enhancing customer profiles, third-party data also bolsters targeted marketing strategies by making segmentation by customer type or persona more precise.

## Advertising to the homebody economy

When executing targeted marketing strategies, an omnichannel approach is more necessary than ever. Right now, brands have the chance to get in front of consumers they may never otherwise engage with because so many marketing channels are in play.

Between social media, OTT, and digital ads, brands should be capitalizing on the availability of the at-home consumer audience. Consumers’ media consumption is at a high, opening up lucrative marketing opportunities — not because there’s necessarily more ad space, but because there are more eyes on screens these days.

Radio and podcast ads aren’t out of the question either — though not as many people are commuting to work, [Nielsen reported](https://www.forbes.com/sites/bradadgate/2020/08/21/nielsen-how-the-pandemic-changed-at-home-media-consumption/?sh=765b07e45a28) that 75% of people say they listen to music during the workday, and 40% of those listeners do daily. In light of this, brands should consider including placing ads where their audiences tune in.

As for offline marketing channels, direct mailers are a great way to deliver personalized promotions to the at-home consumer — at home. It’s been reported that [70% of mailers that were personalized](https://www.sequeldm.com/direct-mail-trends-v) in some way as marketers vied for consumers’ attention. They were even integrated into digital strategies: QR code usage in direct mailers rose 69% in 2020 from 2019, making attribution easier for brands relying on mailers to convert customers online.

## At-home habits die hard

Even as Covid-19 vaccinations become more widely available and businesses are able to return to previous modes of operation and capacity, the shift to a homebody economy is most likely here to stay. [According to McKinsey](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/survey-us-consumer-sentiment-during-the-coronavirus-crisis), 3 out of 4 Americans have tried new shopping habits (changed brands, stores, or way of engaging) and intend to stick with them after the pandemic. So, businesses must continue to adapt because meeting the customer where they are is more urgent than ever before.

People are spending more time than ever at home so brands are adapting to reach them there. Here's data-driven guidance for the homebody economy.

How to reach at-home consumers in a homebody economy

This spring, amidst widespread Covid-related lockdowns, Microsoft CEO Satya Nadella remarked that “we’ve seen two years’ worth of digital transformation in two months.” While COVID clearly shocked the economic system, it also expedited a direct-to-consumer trend across retail and CPG that has been gaining momentum for years.

[Ritual](https://ritual.com/) is one of the brands leading this revolution. Since launching in 2015, it has sold over a million bottles of vitamins through its subscription service. It’s brought transparency, quality, and reliability to an industry ripe with pseudoscience and half-truths. When you buy from Ritual, you can trust the source, ingredients, and science behind their vitamins.

Over the last year, Ritual has worked with Faraday on a few core initiatives to better understand their customers and grow their brand.

## Improving engagement with customer insights and personas

Ritual has used customer insights reports, the [Faraday Identity Graph](https://faraday.ai/features/data), and [custom personas](https://faraday.ai/features/predictions) generated through a [k-means clustering model](https://faraday.ai/blog/k-means-clustering-customer-personas) to break down their customers into logical segments, supporting targeted and personalized engagement efforts. This has resulted in improved customer engagement metrics across their email campaigns.

## Optimizing Google Ads spend with geo-bid modifiers

Ritual runs a sophisticated customer acquisition strategy across major digital, offline, and out-of-home channels; a significant piece of this is search engine marketing. Faraday works with Ritual to optimize their ad spend on [Google Adwords](https://faraday.ai/predict/pipelines/likely_buyers_in_google_ads) by employing geo-bid modifiers produced by the propensity models the brand built using our platform. The geo-bid modifiers are applied at the zip code level, automatically increasing or decreasing Ritual’s bid based on the propensity of people in the area of a Google search to subscribe to Ritual. Using pre- and post-performance analyses, Ritual saw a marked improvement in CPA efficiency across the metros where bid modifiers were activated. After the successful tests, they’ve chosen to roll out the bid-modifiers across all non-branded keyword campaigns, resulting in propensity-based geo-bid modifiers being activated across 10,000 zip codes.

## Driving new product adoption with customer predictions

Ritual has a long history of serving women – its founder Katerina Schneider started the company after struggling to find a source of prenatal vitamins she could trust. Earlier this year, they rolled out a line of vitamins for men. Ritual used Faraday to support this effort, identifying who within their existing customer base had a family member or partner that could benefit from this new product line.

## Check them out!

Ritual represents a company that we love. They’ve brought a great set of products to market, and they use data in a thoughtful and strategic way to improve not only their bottom line, but the customer experience as well. We strongly recommend you [give them a try](https://ritual.com/)!

It pays to get into the nuts and bolts of consumer prediction. From custom personas to geo-bid modifiers, Ritual is taking advantage of AI to drive growth.

How Ritual leverages customer insights and geo-bid modifiers for growth

Headlines abound on the [opportunities](https://www.techrepublic.com/article/companies-are-doubling-down-on-artificial-intelligence-and-machine-learning-due-to-pandemic/) and [dangers](https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing) of machine learning. Now armed with [plenty](https://www.propublica.org/article/breaking-the-black-box-when-algorithms-decide-what-you-pay) of [examples](https://www.nytimes.com/2019/08/20/upshot/housing-discrimination-algorithms-hud.html), the Data Science community is at least aware that our data lakes and algorithms can make life more difficult for real people if not properly used. But the root problem can be summed up in pretty simple terms:

> An algorithm contains the biases of its builder.

Examples of bias include:

- **Reporting bias** _(at the source)_ — Maybe the folks who filled out your online survey were mostly young, male, and computer-literate.
- **Implicit bias** _(the call is coming from inside the building)_ — Perhaps your image of an ideal job candidate fits into a mental archetype of which you're only semi-aware, and that image excludes those who don't match the archetype.

At Faraday, we have a handful of approaches we use to minimize these effects.

## Data sourcing

It's possible to get your hands on a pretty broad range of data points these days. Everything from the basics of home value, age, and income to the more esoteric pet ownership and liklihood to buy high-end men's apparel. **Faraday actively excludes data points relating to protected classes**. Race, ethnicity, primary language, and religion are excised from our database entirely and therefore excluded from our predictive models and audience definitions.

Of course, seemingly-harmless demographic variables can often act as proxies for these features, but our stance begins with the most effective levers we have available to pull. And further on in the pipeline we do check _results_ against features like race, but we do it at an aggregated level using standardized census data (more on that below).

## Model priors

We investigate the examples in our training data before they become machine-actionable profiles. In particular, we look at how well they're balanced by age, gender, and geography against the population at large. If there's a significant skew versus the baseline, we know there's a chance the resulting predictive model will show bias, so we rework the inputs.

## Model predictions

When a predictive model is fully-built and ready to be put into action, we typically assign a predictive score to everyone in the country as a matter of efficiency. But this also allows us to check whole populations against statistics that reflect the inclusion or exclusion of protected groups at a census block group level.

If we see - for example - that the lowest scores are concentrated in the block groups with the highest proportions of particular racial or ethnic groups, we'll know that _some_ bias has made it into the model, and we'll revise it.

The bottom line is that these strategies are only a start, and **the best defense against unfair algorithms is multi-pronged and in for the long haul.** If you have questions about how your own applications might be veering away from fairness, I suggest checking out:

- Google's [Marketers' guide to ML fairness](https://www.thinkwithgoogle.com/feature/ml-fairness-for-marketers) or [ML bias crash course](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture)
- ["Abolish big data"](https://d4bl.org/videos/19-abolish-big-data) from [Data for Black Lives](https://d4bl.org/)
- The machine bias case studies at [diversity.ai](http://diversity.ai/#case)

How to reduce the effects of implicit bias in machine learning, from data sourcing to model predictions.

Mitigating implicit bias in machine learning

For years people have griped about the cost of buying and selling homes through real estate agents. The 6% commission paid to real estate agents has remained surprisingly resilient amidst a broad reduction in other financial transaction fees (think stock trade fees). As a homeowner or buyer, depending on your market, the only way to avoid paying agent commissions is through a for-sale-by-owner process. As of 2018, only [11% of American](https://www.nar.realtor/research-and-statistics/quick-real-estate-statistics) chose to sell their home this away and often the result is a lower overall sales price.

This is where our client [Homie](https://www.homie.com/) comes in. Homie’s mission is to make homeownership easy, affordable, and accessible to all. Its technology platform streamlines the home buying and selling processes to reduce fees and commissions, while buyers and sellers work with a real estate agent. To date, they estimate they’ve saved their clients over \$61MM.

Homie came to Faraday when they were in the process of expanding beyond their current markets of Utah and Arizona into Nevada. They were looking to leverage the Faraday platform to support a few key initiatives.

## Predicting optimal locations for expansion

Moving into a new market is always challenging for a company, largely due to the lack of on-the-ground experience and local brand recognition. Simultaneously, data collected from one market is not necessarily representative of the new market. To overcome these obstacles, Homie uses Faraday's location and market intelligence solutions capabilities to guide their expansion across the mountain west. Specifically, Faraday lets Homie predict which zip codes in their target markets contain significant populations of high-propensity customers (homebuyers and sellers), giving the team a better understanding of where they should prioritize their acquisition efforts.

## Reaching likely buyers and sellers, directly and cost-effectively

Working with their Faraday customer success manager, the Homie team built geo-normalized models that accounted for market variances across key attributes, such as home value and household income. Then, they deployed custom lookalike audiences to Facebook, building up additional training data in their new markets. The results from this work were positive, with Faraday-generated audiences doubling conversion rates and reducing acquisition costs by more than 33%.

_Learn more about [likely buyers in Facebook](https://faraday.ai/predict/pipelines/likely_buyers_in_facebook) with Faraday_

## Check them out!

We are excited about the work [Homie](https://www.homie.com/) is doing to give its customers more transparency and control over the home buying and selling process and while getting all the support they need from a whole team of licensed agents, attorneys, and experts.

How Homie uses Faraday to guide its market expansion strategy and reach likely customers, directly and cost-effectively.

How Homie is using location and market intelligence solutions to expand and lower costs

Over the last decade, fintech firms have democratized broad swaths of the financial services landscape, whether they be the likes of Robinhood and stock trading to Prosper and personal loans or Lemonade and homeowner insurance. Our client [DiversyFund](https://diversyfund.com/) is part of the movement to lower barriers to participation, providing everyday investors access to high-value private market assets, like real estate, by removing net worth and high minimum investments requirements. Its online platform makes investing easy, and its educational content helps to inform first-time investors.

DiversyFund’s Growth REIT was qualified by the SEC in late 2018, and by June 2019 they had established their platform and investment criteria to make a concerted push into the retail investor market. Shortly thereafter, DiversyFund began its engagement with Faraday with the intent of using our platform for a few key initiatives.

## Develop a better understanding of their customer base

Being fairly new to market, DiversyFund didn’t have a great sense of who their customers were or how they differed from one another. Faraday enhanced their data and applied machine learning techniques to surface a range of insights about who their customers are and what makes them unique.

DiversyFund’s insight work with Faraday uncovered a fascinating finding: two clearly distinct customer types — aspirational investors and experienced investors. These two groups had unique demographic and financial profiles and used DiversyFund in different ways.

## Predict investment propensity to optimize acquisition spend

DiversyFund wanted to acquire valuable customers in a cost-effective manner by aligning acquisition costs with investment likelihood. Building off of their customer insights, DiversyFund created two predictive models to target and attract the two types of customers. To ensure they reached them with relevant content, they curated their ad creative and set CPA goals that aligned with each group. By creating individual modeling strategies, and in turn digital audiences, DiversyFund achieved a strong ROAS for each group.

_Learn more about [optimitized customer acquisition](https://faraday.ai/solutions/customer_acquisition) with Faraday._

## Refine and scale their personalization strategy

Serving the broad retail investing market means playing to a highly diverse customer base that requires varied messaging and content.

When leads convert into customers, DiversyFund uses email campaigns to engage with and educate them. Given the varied level of investment acumen across their customer base, DiversyFund needed to find a way to segment their customers to assure the right content reached the right person.

To do this, DiversyFund used the Faraday platform to develop brand-specific personas that let them reach a new level of scalability in their personalization efforts. These personas are assigned at the moment of signup and allow the brand to segment their customers by sophistication level, without requiring a questionnaire or observation of investment activity.
With this deeper level of customer insight, DiversyFund has created marketing drips for each persona group and seen a clear uptick in email engagement and deliverability rates.

## Check them out!

We love [DiversyFund's](https://diversyfund.com/) work and are excited for more people to take advantage of their services. In an age of increasing economic inequality, DiversyFund is using technology and regulatory advancements to open up new avenues of wealth generation for everyday Americans.

How DiversyFund uses AI to better understand their customers and deliver personalized investment content.

How DiversyFund uses data science to democratize real estate investment

Acquiring customers who are enthusiastic about your brand and will support you in the long run is vital to establishing the scalability and longevity of your business. As the first step of building out a loyal customer base, [customer acquisition](https://faraday.ai/solutions/customer_acquisition) is a high priority for marketing teams.

This article explores four ways you can leverage data science to extend your acquisition spend further to build a loyal customer base.

## Targeting predicted high-LTV customers

The fact that it costs more to acquire new customers than it does to retain existing ones is no secret, which makes finding high-LTV prospects paramount to ensuring a successful, cost-effective acquisition strategy that will fuel future revenue.

Knowing your target audience is a key piece of acquiring valuable customers from the get-go. What makes your best existing customers unique? What draws them to your brand and/or products? Defining and identifying high-LTV customers is the first step in predicting who out there will become a valuable customer.

Predictive analytics play a crucial role in identifying traits and patterns in your customer data that are indicative of high-value customers. Of course, the depth and breadth of your customer data affects how accurate these predictions will be. Introducing third-party data is a great way to gain deeper insights, fuel your predictions, and generate higher-performing lookalike audiences.

## Reaching likely buyers through multiple channels

Acquisition campaigns are most effective when orchestrated across multiple channels. [Reaching likely buyers](https://faraday.ai/predict/category/likely_buyers) with consistent messaging through paid social, direct mail, and other targeted channels helps reinforce your brand in their minds and gives them more opportunities to engage.

To reach the same consumers across online and offline channels, you can't rely on anonymized audiences. Instead, you'll need a secure way of building known-identity audiences bolstered by third-party data.

## Using known-identity attribution to optimize your channel mix

A diversified marketing mix allows you to reach varying demographics across different online and offline channels. But the sheer scale of marketing across numerous platforms doesn’t guarantee a high conversion rate or substantial ROI, so knowing where your money is best spent should be a high priority.

Equip your team with the capabilities to closely monitor campaign performance across each channel so you can identify what is working and what isn’t. It’s worth noting that this can be a difficult task with cross-channel campaigns, as several digital channels provide anonymized or aggregated results.

There are several attribution models out there, but we've found that known-identity attribution can provide you with a better understanding of who you should be targeting on each channel. Acquisition spend is precious, and having the ability to — and being excited about — shifting gears to improve your strategy is crucial. Marketing strategies should not be fixed; they should evolve as your customer base grows and changes.

## Delivering customized offers and discounts

Some consumers need a bigger push than others to convert — this is where promotions and discounts come in. But to protect ROI on your campaigns, it's important to predict which customers are will stay engaged after that initial purchase. Delivering a 15% discount to all consumer might help drive conversions and reduce your average cost-per-acquisition, but if they don't come back, you might find yourself in a tough spot down the road.

Similarly, not all consumers need a discount to nudge them along the path to conversion. Using [predictive analytics](https://faraday.ai/features/predictions) to determine who should receive a discount and how big that discount should be can help you beat cost benchmarks and increase the overall return on your acquisition spend.

How consumer marketers are using machine learning to optimize customer acquisition and advertising efforts.

Creative ways to optimize your ad spend with machine learning

The use of third-party data isn't new, but it's becoming a necessity as businesses shift to a customer-centric approach. Marketers everywhere are realizing that first-party data is only the tip of the iceberg when it comes to understanding their customers.

The rise in digital technologies and interactions has increased the need for bringing outside data into their companies. While it may be as simple as understanding your existing customers' household and financial standing, with third-party data you can also gain deep insights into your leads on a known-identity basis.

Overall, leveraging third-party and first-party data adds breadth and depth to your customer insights, informing better branding and messaging decisions, personalization efforts across channels, and taking much of the guesswork out of targeted and location-based marketing campaigns.

## Customer identity resolution

The capability to bring together first-party data from various sources and third-party data to compile a unified picture of each customer is increasingly valuable. Spending dedicated to identity data assets [grew by more than 50% in 2018](https://www.iab.com/wp-content/uploads/2018/12/IAB_The-State-of-Data-2018_2018-12-05_Final.pdf?ref=faraday-blog), to a total of $846MM.

Personally identifiable information (PII) is crucial to connecting the dots across multiple touchpoints (online and offline) to form a holistic picture of your customers and prospects so you can reach them with content that will resonate with them through the channels that matter.

## Using third-party data for personalization

In a time when [almost all consumers](https://www.bondcap.com/report/it19/?ref=faraday-blog#view/148) prefer ads and products that are relevant to them, knowing how and when to serve your target market is crucial to effectively engaging your customers. According to [Forbes](https://www.forbes.com/sites/insights-treasuredata/2019/05/01/the-path-to-personalization/?ref=faraday-blog&sh=5bd84dc7a76b) 46% of marketing executives are not where they want to be in terms of delivering personalization, and 57% say that they hope to advance their personalization initiatives over the next year.

Enriching your first-party data with third-party information on your customers' financial, household, transactional, or lifestyle categories gives you a clearer idea of what your customers look like in real life.

To take it a step further, enriched customer data helps machine learning algorithms build better models. Cluster models, for example, sort individuals into distinct groups based on common attributes. These groups, or personas, can affirm your intuitions about what your customer base looks like, but may also surprise you by revealing significant groups you hadn't known about or previously considered important.

## Using third-party data for ad optimization

If you're using predictive lead scoring capabilities, the accuracy and precision of those scores improve with the quality and quantity of third-party data that you introduce. This not only benefits your leads' experiences, as they'll receive content that better aligns with their interests and where they are in their customer journeys, but it will save you time and money when you're deciding how to prioritize your outreach.

A popular way of leveraging third-party data with predictive modeling for digital marketing is Facebook lookalike audiences. But since the platform shut down advertisers' access to this data in April of 2018, gaining deep insights from Facebook has been a struggle for many businesses that are trying to better understand their audiences and target the right consumers.

If you enrich your first-party data with third-party attributes, you can gain insights on a granular level that you aren't getting with Facebook and other marketing channels. You can then segment known-identity audiences based on certain attributes — or develop in-depth personas for segmentation — and push the appropriate ad creative to those individual segments with the goal of increased, personalized engagement. This goes for not only new leads you're nurturing, but also for the customers you're actively engaging.

Another advantage of leveraging third-party data is the ability to match site's captured emails to real people. This allows you to bucket leads into certain personas for more personalized nurture campaigns that will pull them through their customer journeys with you. Your leads are more likely to receive messaging that aligns with where they are in their individual journeys, and it will save your team time and money when you decide how to prioritize your lead outreach.

## Using third-party data for location-based marketing efforts

Geographic data has obvious benefits, but incorporating additional third-party in geographic analyses improves the quality of the insights. Knowing where your ideal customers live is one piece of the geomarketing puzzle, but gaining an understanding of how your audiences will best interact with your ads offline is achieved by knowing more than just their geographic location.

Attributes like household information, transportation preferences, and lifestyle data helps you prioritize where you place ads, as well as where you might establish new brick-and-mortar presences. A subway ad or billboard placed in your customers' neighborhoods can be a great way to advertise your e-commerce business for those who don't live near a storefront, but for customers who are in your brick-and-mortar trade zones, perhaps a direct mailer advertising an in-store sale would better drive business.

From a creative perspective, audiences can vary widely depending on their physical locations, so the messaging and images chosen for your campaigns should be curated for specific audiences in particular geographies. Providing relevant content increases your chances of audience engagement, so capitalizing on the insights provided by third-party data is a necessity.

This is the case with SEM advertising, which segments audiences by geographic location. With the insights gained from third-party data, you can optimize your SEM spend by targeting areas where higher-value customers reside, spending less on areas that aren't as likely to bring in revenue.

## Using third-party data responsibly

With the rise of regulations like GDPR, privacy and validity of data are prominent concerns when using third-party data and PII. However, there are ways to ensure that your customers' and prospects' privacy is protected. You want to be sure your data comes from a verified source, isn't dangerously invasive, and is leveraged to benefit the customers you're targeting.

Social scraping is a common tactic, and it can provide large categories of data on behavioral tendencies, consumer habits, and some personal information, though this data may not always be entirely accurate. At Faraday we avoid social scraping in favor of canonical sources of information from established third-party data vendors. Vendors like Epsilon verify the accuracy of the high-quality data we license and leverage for our partners.

Faraday's Director of Data Science, William Morris, notes that we sidestep many consumer privacy issues by dealing with datasets that categorize data, such as consumer purchases, by type of product and frequency, rather than the exact stores where those purchases are made. This leads to a significant lift in machine learning models' predictive capabilities without being invasive from a privacy standpoint. Additionally, each of our clients' customer data is siloed using industry-standard practices to protect the privacy of each clients' customers.

Faraday also actively works to build models that are agnostic to a person's membership to protected classes. We exclude potentially harmful categories like ethnicity, religion, or primary language data, as these can disenfranchise certain groups of the consumer population. As an additional security measure, we employ a suite of tools for post-model checking to ensure that attributes that could indicate a person's race or class don't make the models biased or adversely impact a population.

Taking into consideration these types of ethical practices is important when exploring your own use of third-party data.

Using third-party data responsibly benefits brands and consumers alike. Learn how brand marketers are doing it right.

Using third-party data in marketing responsibly 

For nearly every B2C company, revenue is linked to the customer lifecycle. Customer acquisition, engagement, and retention initiatives directly or indirectly impact revenue growth and sustainability by either reducing customer acquisition costs, increasing customer lifetime value (LTV), or ideally, both.

Effective marketing strategies revolve around the customer lifecycle; understanding key lifecycle stages, identifying events that are likely to trigger transitions between stages, and finding ways to optimize interactions across the lifecycle.

Naturally, the more you know about your customers and how to influence them to transition into a desired stage, the more efficiently you can grow and sustain revenue with the resources at your disposal.

Simply put, that's what customer lifecycle optimization is all about: leveraging rich customer data and predictive analytics techniques to generate insights and make predictions that measurably improve outcomes at each stage of the customer lifecycle.

## How customer lifecycle optimization works

Customer lifecycle optimization (CLO) is a practice. As with other practices, CLO involves a series of prescribed steps to be done effectively. While specific lifecycle stage names, data sources, and analytics techniques will vary depending on your industry, company, and objectives, the canonical CLO process comprises four steps: lifecycle mapping; data discovery; predictive groundwork; and implementation and action.

### Customer lifecycle mapping

The first step in any CLO initiative is always identifying and defining key lifecycle stages and transitions between stages. Mapping these stages and transitions to a uniform customer lifecycle is crucial to uncovering rich, predictive data.

The uniform B2C customer lifecycle stages are:

1. Lead generation
2. Lead conversion
3. Retention and expansion
4. Reactivation

While terminology will change from business to business, we found that this formulation is rich enough to capture important boundaries, yet simple enough to avoid stages with ambiguous transitions.

Think about your customer lifecycle. How are stages defined? Which attributes qualify individuals to be placed in those stages? What events trigger transitions between stages?

### Customer data discovery

At each stage, prospects, leads, and customers will complete certain events that will individually or collectively trigger a transition in or out of that stage. Individuals will also have different attributes, which help determine whether or not they belong in any given stage.

Go through each cell of your customer lifecycle map and think about which events or attributes in your data could be used to trigger a transition or pass the litmus test. Then, think about where you can find that data. Is it in your ESP, CRM, or a custom data warehouse?

### Customer prediction groundwork

Once you've formalized your lifecycle and its representation in data, you can start recognizing patterns and eventually predict outcomes. Imagine loading up your converted leads (customers in "retention and expansion") alongside your stale leads that never converted. What differences can you find?

This comparison analysis is especially effective when you've added depth and breadth to your existing data.

As you experiment with this type of pattern recognition, you'll quickly realize that it's the kind of thing that computers do very well. That's where machine learning comes in handy.

### Predictive engagement

Whether you want to increase conversion rates from lead generation campaigns or reduce churn from existing customers, properly targeted outreach is essential to engaging and motivating the right leads and customers to take a desired action.

Outbound communication is the strongest and most versatile intervention at an organization's disposal to compel progress and therefore expand revenue. This includes individually targeted digital advertising, a form of direct outreach.

Having identified a stage transition you'd like to motivate with an outreach intervention, the question becomes, "Who do I reach?" Regardless of the desired transition, the general technique is called audience expansion, also known as "lookalike" audiences.

To apply the audience expansion technique, we must always identify the audience and a set of candidates with similar characteristics and attributes.

With these groups defined, the next step is to apply your predictive groundwork. This could involve using patterns you identified in your data to look for similar opportunities among your candidates, or in more advanced cases, using artificial intelligence to build a predictive model trained to discriminate between likely and unlikely transitioners.

Finally, you will be left with a well-defined group of candidates likely to transition into a desired lifecycle stage when reached with relevant content.

## Putting it all together

To summarize the CLO practice, it's useful to recall the original motivation: leveraging rich data and statistical predictions to optimize revenue-building initiatives throughout the customer lifecycle. This means motivating transitions from one stage to the next.

Customer lifecycle optimization is the practice of leveraging data to maximize revenue-generating customer behaviors throughout the lifecycle. Learn how it works.

What is customer lifecycle optimization and how does it work?

**UPDATED FOR 2017** Now with easy subquery support and a more sensible argument order!

We adapted [this excellent piece by Dmitri Fontaine](http://tapoueh.org/blog/2014/02/21-PostgreSQL-histogram) and turned it into a function.

It can be invoked like this:

```
SELECT * FROM histogram($table_name_or_subquery, $column_name);
```

. . . to give sweet results like this, in a check of the distribution of 2016 political contributions in Vermont:

```
fec=# SELECT * FROM histogram('(SELECT * FROM small_donors_vt LIMIT 50000)', 'transaction_amt');

 bucket |   range   | freq |       bar
--------+-----------+------+-----------------
      1 | [0,9]     | 2744 | ******
      2 | [10,19]   | 5630 | *************
      3 | [20,29]   | 6383 | ***************
      4 | [30,39]   | 1290 | ***
      5 | [40,49]   |  369 | *
      6 | [50,59]   | 3541 | ********
      7 | [60,69]   |  174 |
      8 | [70,79]   |  313 | *
      9 | [80,89]   |  171 |
     10 | [90,99]   |   65 |
     11 | [100,109] | 2363 | ******
     12 | [110,119] |   51 |
     13 | [120,129] |  115 |
     14 | [130,139] |   32 |
     15 | [140,146] |   11 |
     16 | [150,159] |  187 |
     17 | [160,169] |   24 |
     18 | [170,177] |   33 |
     19 | [180,189] |   19 |
     20 | [191,199] |   24 |
     21 | [200,200] |  795 | **

```

Use it yourself by adding this to your postgres setup:

```
CREATE OR REPLACE FUNCTION histogram(table_name_or_subquery text, column_name text)
RETURNS TABLE(bucket int, "range" numrange, freq bigint, bar text)
AS $func$
BEGIN
RETURN QUERY EXECUTE format('
  WITH
  source AS (
    SELECT * FROM %s
  ),
  min_max AS (
    SELECT min(%s) AS min, max(%s) AS max FROM source
  ),
  histogram AS (
    SELECT
      width_bucket(%s, min_max.min, min_max.max, 20) AS bucket,
      numrange(min(%s)::numeric, max(%s)::numeric, ''[]'') AS "range",
      count(%s) AS freq
    FROM source, min_max
    WHERE %s IS NOT NULL
    GROUP BY bucket
    ORDER BY bucket
  )
  SELECT
    bucket,
    "range",
    freq::bigint,
    repeat(''*'', (freq::float / (max(freq) over() + 1) * 15)::int) AS bar
  FROM histogram',
  table_name_or_subquery,
  column_name,
  column_name,
  column_name,
  column_name,
  column_name,
  column_name,
  column_name
  );
END
$func$ LANGUAGE plpgsql;
```

Note:

1. You don't need to use a subquery - you can also just provide a table name as the first argument.
1. Subqueries are useful for sampling. For example, `(SELECT * FROM bigtable LIMIT 500 ORDER BY RANDOM()`. Don't forget parentheses!
1. You can call this as `SELECT histogram()` or `SELECT * FROM histogram()`. The latter form is much more legible!

Happy querying!

Learn how to create in-table histograms with PostgreSQL.

How to do histograms in PostgreSQL

Like many subscription services, meal delivery brand [Marley Spoon](https://marleyspoon.com/) faced the challenge of retaining customers. High churn rates made it difficult to cover acquisition costs and scale revenue, so they needed a way to identify risky customers. To improve their retention capabilities, Marley Spoon approached Faraday to assess the predictive accuracy between traditional segmentation and AI-driven churn modeling techniques.

## Traditional segmentation vs machine learning

With traditional methods, brands analyze trends among customer segments and take action to [prevent those with high churn rates](https://faraday.ai/predict/category/churn_scores) from unsubscribing. This is a relatively reactionary measure. In contrast, leveraging machine learning models to [score leads](https://faraday.ai/solutions/lead_management) and customers for their propensity to churn allows brands to segment customers based on churn risk and properly engage high-risk customers before they unsubscribe.

## Analysis and results

Because we’re most interested in the models’ ability to accurately predict churn, we evaluate performance based on how many churned customers were included in the top 10% and top 20% of predicted churners. Here are the results:

**Churn rate comparison:**

- 19% churn rate for likely churners identified with Faraday churn predictions
- 12% churn rate for likely churners identified through traditional segmentation

**Churn detection lift:**

- 70% lift over baseline with Faraday churn predictions
- 15% lift over baseline with traditional segmentation

To sum it up, Faraday's machine learning [predictions](https://faraday.ai/features/predictions) detected churn 5X better than the traditional technique.

Learn how the meal delivery subscription brand was able to identify likely churners 5X better with machine learning predictions.

All posts