How can you accurately segment customers using data from multiple sources?

One-size-fits-all marketing is a fluke. Thinking and adopting multi-sourced segmentation is quintessential! Netflix realises the power of diverse data by blending viewing habits, ratings, and browsing time; they create ultra-personalized recommendations that keep us binge-watching. Sephora's Beauty Insider program mines purchase history, in-store interactions, and online engagement to craft tailored offers and experiences that keep customers feeling fabulous. You get deeper customer connections, increased loyalty, and marketing that resonates perfectly.

It is important to capture data from different sources and personas to develop fair and reliable information about customers. It also helps you to segment your constructed personas with their unique needs to help you develop strategies to solve their problems.

Accurately segmenting customers involves defining the purpose of segmentation, collecting diverse data from various sources, cleaning and integrating the data, identifying key variables, choosing appropriate segmentation methods, applying these methods, validating and refining the segments, creating detailed customer profiles, tailoring strategies for each segment, continuously monitoring customer behavior, and establishing a feedback loop to adapt strategies based on performance indicators. The process aims to understand and categorize customers effectively, allowing businesses to tailor their approaches and offerings for improved engagement and satisfaction.

Utilising multiple sources of data for customer segmentation provides businesses with a comprehensive, accurate, and nuanced understanding of their customers. Examples of advantages to be gained are enhanced Accuracy leveraging data from various sources, such as transactional data, demographic information, social media behaviour, and customer engagement metrics, can give a more comprehensive understanding of them Comprehensive Insights by using different data sources provide different perspectives on customer behaviour. Combining sources allows businesses to gain insights into their customers' preferences, purchase patterns, and interactions across channels.

Using multiple data sources for customer segmentation is crucial for accuracy and depth. It prevents reliance on potentially flawed single-source data, offering a more comprehensive view. Cross-validation of trends reveals consistent patterns. It leads to finer, more detailed customer segments, reflecting the real diversity and complexity of your audience. With richer insights, you can tailor your marketing messages more effectively, making them resonate with each specific segment. This multi-faceted view also keeps you adaptable, ready to respond to market changes and evolving customer preferences. In short, it's about making smarter, more informed marketing decisions.

Segmentation is typically demographic. But demography is not why people buy your product. Begin by creating unique Hypotheses why people buy your product. Explore using Consumer Trends that neighbor your industry. Next , run primary original Qualitative research to explore the Why? of you customers. This leads to formulation of personas, which can be psychographic or by motivations. Next, Run an original Quant research to quantify the personas jnto segments. Make sure to collect demographics and other markers such as income, urbsn/suburban etc, in order to be able to extrapolate segment size. Also, important for your Quant to use same markers as in the genericmarket reports - this makes sizing more accurate.

Demographic data: Combine demographic data like age, gender, location, income level from surveys, sales records, website analytics etc. to segment customers into groups with common attributes. i.e.. young suburban families, affluent urban professionals, retirees, etc. Purchase behavior: Analyze purchase history, frequency, monetary value, products purchased, brand loyalty from sales data, website transactions etc. to segment customers into groups like big spenders, frequent small purchasers, impulse buyers, bargain hunters etc. Web behavior: Use website behavioral data like pages visited, clickstreams, on-site search terms etc. along with demographic data to segment visitors into prospects, close to purchase, channel preference etc.

It is important to determine the variables you would like to measure, for example; age, place, time on the internet etc. Creating a dataset where you can input and analyse these data from different sources. It is important to collect data based on unique features the technology or technique provides.

The sources of data depend on the nature of the business. I personally used combinations of: - Market research (quantitative and qualitative) - you can do desk research or employ an agency to research for you - Published industry reports - Official information from state agencies - Analytics from your website - Data from CRM - Analytics from Social Media

Whatever the sources may be, if you source externally - chances are that automation has been used somewhere to gather the data, so quality will be various levels of 'variable'. There is no substitute for data that is 'qualified by voice' and 'qualified within your own organisation'. The main downside is that it can take a lot of time and man power to gather and input into your CRM. Surveys can also add value if you have Surveyors with Sales experience in the Survey team. It should be in every brief that where possible, Surveyors can generate Sales leads directly. Particularly useful for building early-stage lead funnel prior to a product launch (for complex, high value solutions with long sales cycles).

Real Time CDP (Adobe Experience Platform) is a good tool that can be used to create segments using data integrated from multiple sources. It provides a single line of truth from the multiple touch points.

Ultimately this is about data convergence, you need to be able to tie as many data sources as possible together onto one record. The key to getting that right is working out your unique identifiers, today in a crm this if usually an email. Everything else then needs to find a route back to that record. I would very much steer away from some of the tools mentioned here like Zapier and Stitch though. On the face of it they make sense but they are often unsupported integrations (meaning they break) and they are too flexible so you typically end up with quite a bit of technical debt. I have seen them used lots of times because it’s the quick and easy way to integrate but they never work as well or as reliably as a native integration.

Multiple sources can be a double edge sword. Though theoretically, looks more reliable as not to rely on single source, the verification process is tedious as you need to verify Multiple sources and this could trigger misleading interpretation on the data. Instead, from the beginning of any new product launch or business startups, ensure data entry into ecrm or company database portal are 100 percent accurate especially phone nber and email address to be a useful date.

The following must be done to integrate data from multiple sources: 1. Define Clear Objectives 2. Select right integration methods 3. Focus on Data Quality 4. Make Data Scalable 5. Security and Compliance must be priority

Having a single dashboard helps a lot. And today’s tooling capabilities makes this quite easy. Using tools like Looker Studio or Retool where you can create a dashboard reflecting your database, without impacting it real-time, is a robust way to have a standardized dashboard for marketing teams. These tools also allow you to connect third-party data into the dashboard with hundreds of integrations available. The best part is that you can even run SQL queries inside, which makes your job even easier when you have to aggregate data by joining multiple tables.

Combining multiple data sources begins with defining the benefits your product provides and what customers would be most interested in your product's benefits. If you start with the value, you‘ll see which demographic profiles benefit most. Next, identify 3-5 ideal customer profiles and list why they want your product. With these attributes in mind, you’ll see the common data sources. Tag these based on the buyer-user-decision journey most relevant to your funnel, then customize your marketing to deliver relevance. This is most easily done on import/merge using tags. Be sure to map any tags to the funnel stage top/middle/bottom for the best measurements on how your efforts are performing then, rinse, repeat, and optimize as you learn.

Customer segmentation from data ocean is very crucial task. It needs to follow below steps- 1) Defining criteria 2) Creating funnels 3) Filtering relevant data 4) Bifurcation of nearly accurate options 5) Designing rough segmentation 6) Arriving at final segmented model All these steps needs to be followed meticulously using both Qualitative and Quantitative methods. Bifurcation based on defined parameters is crucial and we need to take care of demographical & psychographical perspective of diverse customer datasets.

I usually go about looking at Hypothesis for who would be expected target audience for the Product and which one should be deriving value: Based on this, I segment customer cohorts and look at usage patterns for each cohort and whether the hypothesis is holding through or is there some surprise elements. Surprise elements sometimes can be double clicked and followed to gather additional understanding. This can result in additional use cases for rest of cohorts, future product roadmap and going next level in driving adoption. Your customer segmentation can be based on overall product or even split by different use cases/ widgets/ screens you have (like sentiment analysis, vs competition benchmarking vs self tracking)

I personally use webhooks for marketing channels to send / receive data to be modified within the database. As the database is also connected to the CRM, all the data coming from different sources can be easily utilised to create custom cohorts automatically for several purposes such as retention, onboarding and so on. Steps: 1. Integrate data from CRM, analytics, and social platforms. 2. Create customer profiles with demographic, behavioral, occupation data. 3. Set segmentation criteria (age, location, purchasing behavior, etc.). 4. Group customers using analytical techniques like cluster analysis. 5. Validate segments to align with business goals. 6. Formulate targeted strategies for each segment through your CRM software.

Use your customer segments for product marketing after segmenting your customers with data from various sources. Understand the value proposition of your product or service for each segment and target the segment accordingly. It is important to ensure appropriate messaging format and channel while reaching out to the customers.

After segmenting your customers with data from multiple sources, you need to use your segments for product marketing. Understand the value proposition of your product or service for each segment and tailor your marketing strategy accordingly. Positioning is the way you define and differentiate your product in the market and in the minds of customers, highlighting your unique selling proposition. Messaging should be crafted and customized according to the language, tone, and style of each segment, addressing their pain points and goals. Select and optimize channels according to the behavior, habits, and preferences of each segment. Design and test offers according to the value, loyalty, and potential of each segment.

In my experience, combining data from diverse sources unveils a comprehensive view of customer behavior and preferences. Once, by merging CRM data with website analytics, we uncovered intriguing patterns. This amalgamation allowed us to understand not just what customers bought but also their journey before purchase—the pages they browsed, time spent, and the content that intrigued them. This intricate data tapestry empowered us to craft precise, targeted campaigns, tailoring product marketing strategies that resonated deeply with distinct customer segments.

When we complete task of customer segmentation, it comes next to utilise the same for Product Marketing. Here, proper data analysis helps to create full proof product marketing strategy. Identify market segments of interest, defining product for target market and designing strategies to achieve goal is essence of using customer segmentation data for successful product marketing.

If you are confident about the buyer and user personas, this step is very seamless. And of course test the outcome for each segment. Maybe changes in the message in tonality or brevity might be a game changer.

In the realm of customer segmentation, leveraging behavioral data offers a dynamic perspective. By analyzing patterns in purchase history, engagement levels, and interaction frequencies, businesses can uncover hidden customer preferences and tendencies. This approach not only refines segmentation but also enables more personalized and effective marketing strategies. It’s crucial to continuously update and refine these segments, as customer behaviors and market trends evolve, ensuring that the segmentation remains relevant and actionable.

Contrary to the article, the best way to accurately segment customers involves leveraging a Customer Data Platform (CDP) that integrates various data sources seamlessly. Utilize a CDP to aggregate and harmonize data, ensuring consistency and accuracy. Employ machine learning algorithms to identify nuanced patterns across diverse datasets, enabling more sophisticated and precise segmentation. This approach minimizes manual efforts, reduces errors, and provides a unified view, ensuring a more nuanced understanding of customer behavior and preferences for tailored marketing strategies that resonate effectively.

Segmenting customers using data from various sources can enhance accuracy by providing a more comprehensive view. Integrating data from sources like demographics, pattern of purchases, behavioral attributes, and deeper interactions allows for a holistic understanding of customers. However it is very important to ensure data quality, consistency of information, setting logics and robust analytics might help understand the right method of crucial segmentation.

Define Segmentation Criteria: Clearly outline the segmentation criteria based on your business goals (e.g., demographics, behavior, preferences). Collect Relevant Data Gather data from various sources, such as customer databases, CRM systems, website analytics, and social media platforms. Clean and Standardize Data:

This requires integration of quantitative and qualitative research. Quantitative research can include things such as TAM, CAGR, etc by market size. However qualitative is the harder part that requires understanding the customer needs and your strength to serve each segment. In order to develop this understanding you must get close to the segments you are targeting understand the voice of the customer and their drivers in order to communicate your value proposition in a compelling manner to your target segments.