online user segmentation

Online User Segmentation – Overcome Data Overload

Posted on Posted in 1. Sales Tracking

Making sense of online user data to build target customer segments is a key challenge for today’s marketers. Online user segmentation enables marketers to build relationships that benefit not only their organisation, but also visitors and customers.


How can we effectively use online user data when building marketing campaigns? The massive amount of online user data has reached the point at which we arguably know too much about the customer. How do we determine what are the most important data points to build target customer segments? What should the size of the target customer segment be in order to not be too niche? Is it possible for us to create dynamic marketing creatives that are user specific?

 

Online User Segmentation

Knowing who your customers are and what they need is a requirement to compete in today’s competitive online world. Traditional customer segmentation is at the heart of every marketing organisation, giving companies a clear understanding of the most attractive segments based on size, profitability, and growth potential, to better align and meet client and customer needs. Anticipating what they want and providing value at the right time, place and format is key to win.

Due to a deluge of data, marketers are no longer trying to fit large groups of people into known categories, but use micro-segmentation to build increasingly finer market segments.

Engagement funnel

For instance, online shoppers enter the top of the engagement funnel as separate and unique experiences. These unique experiences include customized variables such as targeted messages, custom design, and relevant offers within lead processes. The goal is to continually optimise such variables at a segment level based on behavioural, psychographic, and demographic data.

Essentially, optimisation depends on the purpose and goals of the landing page. Four types of sites require different behavioural data to build segments.

 

Lead Generation sites

These entail a focus on optimizing variables around behaviours that eventually lead to the closed sale. For instance, by understanding actions taken prior to making a purchase, marketers can effectively remarket to segments. Such behaviours may include:

  • Viewing product demos or product-related material;
  • Viewing product pages;
  • Filling in inquiry forms;
  • Downloading product information; and
  • Holding a meeting with a company representative.

 

Media sites

These seek to ensure long-term satisfaction and loyalty by increasing relevance in communications and offers. Hence, marketers can pursue improved ad targeting and product recommendations with extensive insights on customer interests based on:

  • Referring domains and search keywords to overall traffic volumes;
  • Unique page views;
  • Conversion metrics such as newsgroup sign-ups, alert features and chat; and
  • Campaigns that succeed in driving desired traffic to the site.

 

 

Support sites

These key in on the visitors ability to find specific kinds of information to reduce churn risk. Building segments for support sites, marketers may want to look at:

  • “Information found” conversion rates by various visitor groups;
  • Keywords and phrases requested by new visitors; and
  • Time spent on top pages by new or returning visitors.

 

 

eCommerce sites

Common goals include increasing the frequency of orders and average order size through understanding of visit patterns and cross-sell potential. Moreover, product search may reveal segment evolution trends as guidance for decisions to expand into new product lines, i.e.:

  • Offsite product search (search engine queries and keywords);
  • Onsite product search;
  • Product page views;
  • Shopping cart additions, abandons, and removals, as well as process stage abandons;
  • Selecting varying shipping and handling rates;
  • Response to order confirmation upsells; and
  • Entering promotional codes.

 

Making User Segmentation Work

Improved segmentation can lead to significantly improved marketing effectiveness in terms of cost savings, cross-selling, better advertising results, customer satisfaction, etc. Building and maintaining an effective micro-segmentation framework, marketers need to consider the following:

1. Determine scope: Define business expectations, use cases, and target customers (e.g. active customers, new customers) to ensure that produced outputs are relevant and no duplicate work is required afterwards.

2. Automate data preparation: Utilise automation tools and techniques to save time, minimize human error, and dedicate resources to more value-adding activities.

3. Ensure data quality: Address issues of data quality, availability and distribution prior to segmentation to avoid creating unreliable or unstable segments.

4. Mine for business value along with statistical significance: Combine business inputs from end-users (e.g. behavioral hypotheses, prioritized attributes, strategic objectives) with sound algorithms (e.g. K-Means, SOM) to ensure early adoption and potential to drive action.

5. Monitor profiles to discover segment evolution: Monitor key segment attributes and migrations to understand customer compositions, uncover revenue potentials, and refine segment strategies for proactive management of customer retention and value.

Apart from activity-based data based on the website, marketers have access to unstructured data such as social sentiment data in terms of product associations (e.g. likes or follows), online comments and reviews, and customer service records. Hence, marketers can benefit from social CRM and integrating social media analysis with business intelligence to gain insight into online activities that have an impact on business.

 

References:

Forte Consultancy (2015). E-Commerce Customer Segmentation. forteconsultancy.wordpress.com

Kumar, V. & Reinartz, W. (2006). Customer relationship management. A Databased approach. Wiley.

Million, M. (2014). Customer Segmentation in the Age of Big Data. fullsurge.com

Chertudi, M. & Marshall, S. (2007). Online Marketer‘s Segmentation Guide. omniture.com

Simon Raun Madsen

Simon Raun Madsen

I am a marketing, business, and communications academic and practitioner with strong cross-disciplinary skills from courses and work experience within the fields of marketing and business intelligence. In particular, my passion for technology and several years of experience with online marketing have provided me with a flair for digital solutions, data analysis, and front-end development.
Simon Raun Madsen

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