What Does “Bad Data” Mean for eCommerce Personalisation?

Analytics
What Does “Bad Data” Mean for eCommerce Personalisation?

You think you know everything about your customers when you gather all the data that you can. But what if you don’t actually know them that well, and the data you collected is “Bad Data”? 

Key Takeaways

  • Bad data is any inaccurate or missing information regarding customers, goods, or stores that can negatively affect customers’ shopping experiences and the profitability of an eCommerce company
  • Outdated data can lead to irrelevant or ineffective personalised recommendations and experiences.
  • To prevent bad data, use data governance frameworks to ensure that data is collected, stored, and used ethically and transparently.


    Let’s get on with the article to discover what “Bad Data” means for eCommerce and how to avoid it.

    The Problem with Personalisation

    Bad data is any inaccurate or missing information regarding customers, goods, or stores that can negatively affect customers’ shopping experiences and the profitability of an eCommerce company. Wrong phone numbers, improper email addresses, or inaccurate postal mailing addresses are bad data examples.

    eCommerce is a rapidly changing industry, with incredible advances in machine learning and AI changing the game constantly. In addition, customers have a growing expectation for immediate satisfaction and expect digital services to streamline the online shopping experience. That all makes for one harsh industry to succeed in. Remaining agile is key.

    People may visit a site to book a jungle safari holiday with your company once, but that doesn’t mean they want to be inundated with offers for more jungle safari packages every time they visit your site.  Offering outdated recommendations based on older data will only frustrate the customer, with 95% of customers likely to leave your site if they get poor search results.

    What is Outdated Data?

    Outdated data occurs from:

    • Inaccurate data 
    • Poor data quality 
    • Inconsistent data

    The data regarding your customers is always evolving. It constantly has to be gathered, transformed, and updated. Additionally, each stage in the data lifecycle presents a chance for errors or inadequate procedures to reduce the data quality. 

    Therefore, the fight against bad data quality is never-ending. However, you’ll have a solid foundation for high-quality data by being aware of these probable reasons and using the appropriate technical assistance.

    What is Inadequate Data?

    Inadequate data can occur because of:

    • Lack of data
    • Too much data
    • Not enough data points

    To understand what our customers want precisely, we must know where they are coming from when they arrive on our site. This requires greater insight than their on-site behaviours.

    You must have clear access to the channels they are referred from, including information on:

    • Social media channels
    • Organic search results
    • Whether they are first-time visitors or frequent shoppers
    • Their favourite categories
    • Their preferred brands

    When we look at the data recorded by analytics programs, two complementary analytics metrics let us know more about a buyer’s journey: Click data and engagement data.

    These are often confused. Although click data tells companies what pages on their website are performing well, it is difficult to know what the customers are really interested in without the engagement data.

    Having inadequate data that limits the insights on the customer makes it harder to offer relevant product recommendations. Many eCommerce companies overlook the value of this non-click behavioural data, which means their efforts for eCommerce personalisation are hampered by only having part of the picture.

    What is Bad Data?

    Bad data is the result of:

    • Unreliable data sources
    • Biassed data 
    • Fraudulent data

    Unreliable data sources can be caused by human error with manual data entry. These errors might range from a typo to an entire entry being overlooked. A person might accidentally enter information in the incorrect field. Therefore, it is essential to automate data entry and try to stay away from manual data entry.

    Another point that causes bad data is biassed data. Omitted variable bias occurs when vital characteristics that affect the result are absent from the data. Typically, this happens when important qualities are not accessible to the mechanism recording the data or when data production depends on human input.

    In addition to errors, a lack of a common understanding of how the data should be gathered, converted, stored, or displayed is another reason for poor data quality. The meaning of each entry can be ambiguous. But due to the unusual admission procedures, they won’t be grouped together. Therefore, you must establish and maintain company-wide standards to prevent concerns with consistency. Better to programme your automation tool to detect inconsistent data entries and update them automatically.

    Examples of Bad Data in eCommerce Personalisation

    An example of bad data would be customer contact information, such as email addresses or phone numbers, that have changed. When the customer information is changed and the database hasn’t updated it, the personalised recommendations can be irrelevant.

    Another example can be that customer preferences or interests have changed over time or the purchase history can no longer be relevant or accurate. In this case, websites should always update their customers’ previously clicked items and update it accordingly. 

    Impact of Outdated Data on eCommerce

    The impact of outdated data can lead to irrelevant or ineffective personalised recommendations and experiences. Customers would not get relevant items recommended which would not make them satisfied and decrease loyalty with the eCommerce website. 

    Furthermore, outdated data can also negatively impact data analytics and reporting. When analysing customer lifetime value, the value might have changed overtime due to outdated data and the website becoming irrelevant to the customer.

    How to Keep Your eCommerce Personalisation Relevant

    Use Multiple Data Sources

    Using multiple data sources to check the quality of your data is essential. In addition, always save your data sources as well. To have good data analysis skills, you should first question the data and use clean data for the best results.

    Use Data Validation and Cleansing Tools

    The automation of validation and cleansing tools is necessary to avoid bad data. Maintain ongoing reviews of fresh sources while confirming the accuracy of the product information. In addition to the supplier’s requirements, search for other sources of information for this purpose. These can contain descriptions of products comparable to those you currently own or even web pages for different online retailers.

    Use Real-Time Data

    Companies must understand the needs of their customers in the moment and respond to their behaviours to make real-time recommendations. The more data you acquire and analyse, the more accurate you will get with your real-time recommendations. And personalisation increases the chances of conversion by 75%. For successful eCommerce personalisation, your company must have a recommendation engine that instantly recognises visitors. It also must be able to serve up personalised product recommendations that the customer still wants.

    A/B Test Your Personalisation

    The best way to double-check your data is A/B testing your personalisation. Running A/B tests can be done on the same segments or different segments. There are many different types of segmentation, ranging from simple demographic segmentation utilising factors like age and gender to more complex differentials like purpose and life cycle stage. This way, you will always be checking your data inside your website.

    Keep Your Personalisation Up-to-Date

    If your personalisation engine continuously collects and memorises visitor data, your personalised product recommendations for each visitor will become more accurate over time. As it gathers data, the algorithms will get more competent and boost revenues by 20% or more.

    By putting the customers in focus and analysing their behaviours on the site, eCommerce companies can learn a lot about how to serve their customers. The more data you gather, the more refined and accurate your service will become.

    Wrapping Up

    Understanding your customer is crucial for an eCommerce business to grow. Companies can further understand their customers’ needs, wants and behaviours by collecting relevant data. However, it is essential to have clean and real-time data whilst analysing the collected data. 
    The Real-Time Conversion Analytics tool by Segmentify is a great way to keep track of your customers’ behaviour and analyse their data. You can book a free trial to try out Segmentify’s personalised solutions to get ahead of the competition.

    Unlock New Levels of eCommerce Growth with Segmentify’s Smart Solutions

    Book Demo

    Free trial

    14 day free trial

    Up and running

    Up & running in 5 days

    Results guaranteed

    Results guaranteed