The promise of personalization is alluring: imagine a complete one-to-one experience for every customer, completely optimized and driven by every data point collected about that person.
This vision has long been a collective pipe dream among marketers, and yet achieving it has been impossible for many. There are many reasons for this: personalization as an aspect of marketing strategy has largely not been well-defined – thus creating misaligned expectations for the practice. Furthermore, accessing the data required to do personalization and scale is still something that many organizations struggle with.
In this two-part blog series, we’ll walk you through the end-to-end process for developing a personalization strategy, including determining how to slice and dice your audience, an overview of the tools you need to bring your strategy to life, and how you can overcome some of the biggest personalization challenges that B2C marketers face today.
Let’s first reframe our understanding of personalization
At Simon Data, we define personalization as any experience that is delivered to a person based on known data about them. By that definition, personalization can exist on a spectrum: it can be one-to-many, one-to-few, or one-to-one.
The companies that are getting the most ROI from personalization know two secrets: First, successful personalization relies on your ability to use data to answer questions about your customers. And second, in order to see value from personalization, the strategy has to be inherently customer-centric, rather than business centric.
Here’s what we mean by that: Your business goal may be something like “increase the number of second purchases within new customers,” but a customer-centric approach requires you to work backwards from your customers’ perspective. You need to understand the different segments that exist within that new customer audience, and the reasons they are or aren’t making another purchase.
Building a customer-centric personalization strategy begins with obtaining a deep understanding who is interacting with your brand, and identifying how and why they differ from one another.
There are reasons why some people take one action, and others don’t. Your job is to uncover why those differences exist.
By doing so, you can use those insights to create relevant, personalized experiences that provide value to those cohorts and drive the action you want them to take.
If you’re feeling overwhelmed by this task, we get it. After all, there are tons campaigns to personalize across channels and a myriad of ways to divide up your audience.
But instead of personalizing all the things, or focusing on only what’s easiest, (i.e. %First Name!), you should turn to your customer data to identify the largest or most valuable customer segments, and the problems that matter most to these.
Focus on solving those customer problems first. As personalization is used to solve one problem, then the next, and so on – you’ll eventually end up with a net-different experience that’s personalized for most of your audience.
This leads to all the metrics that CRM marketers love to see: increased revenue, higher LTV and AOV, and ultimately increased engagement for longer periods of time, and across all marketing channels.
Step 1: Determine your key customer segments
The first step is to uncover which of your audiences and experiences are a good fit for personalization.
Most marketing platforms offer some form of pre-built segments – including us. These can be created using predictive models, or your available data via combining customer attributes and behavioral data to produce segments such as: high LTV, cart abandoners, shoppers based in a specific location, category specific shoppers, discount shoppers. The list goes on.
While these out-of-the box segments offer a ton of value for resource-strapped teams, if you have the bandwidth, you may find more benefit from starting from scratch.
After all, your brand and your customers are unique, and the way you define your most valuable customers may differ from others.
If you need a place to start, check out the above list of 30+ questions you should ask about your customers to inform your segmentation strategy. The answers to these questions should inform the way you build your segments, and illuminate the experiences for which you want to personalize.
The list of questions fall under three main sections: who are your users, what are their needs, and what’s their experience. Here’s a peek below at some of the questions to consider:
Who are your users?
- Who are the general sets of users engaging with your brand?
- What makes your users different?
- What data describes or highlights their differences?
What are their needs?
- How are your customers’ needs different?
- What are they trying to achieve?
- What problems are they trying to solve?
- Do they have different goals from other users?
What’s their experience?
- How is a single, static experience not relevant to them?
- What parts of their experience are misaligned?
- At which points in the customer experience do the largest number of users fracture?
Step 2: Design personalized experiences for each segment
So you’ve answered a bunch of questions – now what? You need to turn the answers to those questions into an actual strategy. The problem is that many of our clients find that organizing their thoughts in response to so much data can feel overwhelming.
To aid your thought process, we built a template to help our clients organize their thoughts around personalization campaign ideas.
Initiative: Brief description of the high-level objective
Goal: Sentence outlining the specific or measurable goal or task at hand.
Segment or cohort
Name of the segment or cohort you want to target
and/or statement to define the segment
|What differentiates them from other customer segments?||What’s motivating them to act?||Which data points describe or highlight their attributes?||Where does that data live?|
Can be within your cloud data warehouse or other sources
|How is the current static experience not relevant to them?|
List out solutions or ideas for personalizing the experience.
One important caveat: this is a simplified approach.
In reality, each statement should be aligned with quantitative and qualitative data. For instance, if you’re using data to define your segments, then the key differentiators should reflect the factors that define each segment or include additional metrics to further profile each group. Here it is in practice using our previous example of increasing second purchases among new customers within the first 30 days after their first purchase.
Initiative: Increase Customer Lifetime Value
Goal: Increase second purchases among new customers, within 30 days after first purchase.
Segment or Cohort
|New Product Finders|
First purchase date is within 30 days ago AND purchased product SKUs were added within the last 90 days
|User is primarily interested in new products||User wants to impress family and friends with the latest style or gadget||New user flag|
Price point affinity
Last product or category viewed
Promo code redemption rate
Email and/or SMS engagement
|Ecommerce / POS data|
|Majority of new customer experience focuses on promotions rather than new products|
Email and SMS messages focused on new products
Dynamic homepage content promoting new products
Retargeting ads promoting new products
|Single Minded Shoppers|
First purchase date is within 30 days ago AND user has at least 2 sessions browsing [product category] AND activity date is within 30 days
|User is shopping for one type of product or product category||Users want to save time by quickly finding and purchasing a known product|
User is looking to make a high-consideration purchase and is doing research
Ad, email, SMS and homepage messaging are not tailored to users
Dynamic email and/or SMS comms based on product affinity
Dynamic homepage promotion and retargeting ads promoting add-ons or related products based on previous purchase
Product or category specific-content to aid research for high-consideration purchases
First purchase date is within 30 days AND user redeemed promo code OR engaged with discount emails
|User only converts when products are on sale||Budget conscious users that want to buy premium products while getting a great deal|
Not every user reacts to the same promotions the same way
Dynamic discounts ranging from 10-35% sent to top priority customers based on engagement, AOV, and LTV
Dynamic homepage content promoting latest discount
So if you’ve made it this far – you’ve established your most important segments, and have come up with viable personalization strategies. Now the only remaining task is to determine where to start. Prioritize your campaigns by comparing the perceived level of effort required to launch the desired experience for each group against the estimated return on investment (ROI).
Step 3: Identify the data you need to launch your campaign.
Grappling with personalization often forces you to come to terms with some very hard truths pertaining to the data you have readily available.
So much of personalized marketing depends on sending the right message to the right person at the right time – all of that can be impossible if your data is a mess, out of date, requires you to submit a ticket and wait for your data team to respond – or, just plain inaccessible.
Because you’re building a personalization strategy that’s based on the idea that different customers need different experiences, the data you collect should help illuminate the differences that exist between customers.
Identifying the exact data points or attributes you need to drive these differentiated experiences depends greatly on your business, your products, and your customers. But generally, it falls into a few categories:
- Engagement Data – Where and how did they engage? e.g. Mobile/iOS, Email, In-App
- Identity and Location Data – Who is the customer and where are they located? e.g. Customer Name, Email Address, IP Location, MAID
- Event Data– Did an action occur or not occur? e.g. Click, Abandoned cart, Abandoned check out, Purchase
- Event Context – What information describes the event? e.g. SKUs, Campaigns or Promotions
Raw data alone isn’t always the most useful. Using a customer data platform (CDP) helps transform the raw data from your data warehouse into unified customer profiles, which enables your data to be more useful and descriptive of the characteristics of your customers.
The importance of contextual data for personalization
Contextual data is information that provides a broader understanding of an event, person, or item.
Segments are often built around high-aggregation data attributes—such as whether someone is a new or returning user—but the more contextual data you gather about your customers, the more effectively you can target and time your personalization campaigns.
Contextual data can influence which segment a customer falls into, the timing or frequency of your messages, the channels you use to deliver the message, and the context of the message itself. This type of data can include things like weather, traffic location, seasonality, past purchases, preferred channels, and more.
Think of your segment as the main ingredients of a recipe. Contextual data acts as the spices and flavors that elevate the dish to a new level. Just as the right combination of spices can transform a dish from ordinary to extraordinary, gathering comprehensive contextual data about your customers enhances your ability to craft targeted and well-timed personalization campaigns.
Examples of contextual data
- Preferences: What are their shopping, communication, and channel preferences?
- Content/Product Affinity: What content or product types have they engaged with or purchased?
- Attribute Affinity: What descriptive attributes of the content or products do they gravitate towards?
- Price Point Affinity: What relative price points have they bought or browsed?
- Time Indicators: What is the time of day, day of the week, and/or month of the year?
- Geographics: Where are they? Are they always in the same location or different?
By no means is this a definitive list of all the contextual data you should collect about your customers, but hopefully, it helps you start thinking about where you have gaps in your current state. The data you collect should help quantify all of these differences about customers. By using the strategy planning template as a guide, you can determine if you have the necessary data to segment your customers in order to support your goals.
But of course, a solid set of customer data is only the beginning and having raw data alone is not very useful – the true value comes when you transform this data to be more descriptive of characteristics of your customer, and establish a strong technical foundation to activate this data and bring your personalization strategy to life.
Your technical foundation for personalization depends greatly on your company’s data infrastructure, available technology and configuration, budget, revenue goals, the way your internal teams are organized, and data governance. In our next post, we’ll dive into how navigate all of those things when making investments into personalization technology, such as CDPs, email service providers and more.