How to Leverage Predictive Marketing for Ecommerce Fashion Brands

How to Leverage Predictive Marketing for Ecommerce Fashion Brands

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The world of marketing has evolved over the years and predictive marketing is the key to success for all brands, including fashion brands. If you are frustrated with your existing marketing campaigns that are not yielding the desired results in terms of profitability and customer engagement for your e-commerce fashion brands, it is time for you to make some changes in your marketing strategies. Including predictive marketing models to your advertising campaigns is the solution for the same.

Leverage Predictive Marketing for Ecommerce Fashion Brands

Understanding Predictive Marketing

The State of Predictive Marketing Survey report published by Everstring in 2015 mentioned that 91% of successful businesses adopted the predictive marketing model.

If you have not heard about predictive marketing, then the best example for it is when you check any product on any e-commerce site, you will see that when you open any other website, the same product will be featured as advertisements on the side or the bottom of the page. This can be achieved with the help of predictive machine learning algorithms, artificial intelligence (AI), and similar predictive data analytics tools. 

Predictive marketing utilizes high-end algorithms based on statistics that correlate the consumer's past buying pattern based on month, day, etc., and a generic trend of customer behavior. For e.g., one doesn't need to know in-depth about AI to predict that the sale of expensive ethnic wear increases during the festive season. However, if the fashion brands can predict the trends in vogue among different age groups, they can stock up well in those categories and stop the customer from going to the competitors.

In order to leverage predictive marketing, let’s look at the three models used in this field:

  • Cluster models: If you can reach out to the right kind of target audience through push data and email campaigns, half of the battle is won. This model filters the audience based on their history of brand engagement, age, demographic location, buying history, etc.
  • Propensity Model- This model is not accurate as it tries to predict whether the customer will finally buy the product or will just check it out and add to future wishlist or simply disengage. Historic data shows that most of the customers check out multiple e-commerce sites before they make their final selection. The predictive marketing models, after churning data for the past few years have also found that most of the customers plan to buy something much in advance and try checking out various websites months before their actual purchase except the FMCG products.

Propensity models will help the e-commerce fashion brand to gauge if the customer will finally end up buying the product which they checked out several months back.

  • Recommendations Filtering- These models offer recommendations to the customers based on the buying pattern of other customers who bought the same product in the past. For example, if a customer buys a Kurta, often the recommendation model will suggest matching leggings/salwars, accessories, bags, jewelry, etc., to go with the entire look.
Understanding Predictive Marketing

How to Leverage Predictive Marketing for Ecommerce Fashion Brands

While the marketing team re-designs your entire marketing campaign, they will include the following components to ensure that your topline increases with time.

1.   Predictive Lead Scoring

Predictive lead scoring will give a better idea about the customer behavior and identify which ones are most likely to convert into a sale. It also includes the customer experience in the previous interactions with the e-commerce portal. Many loyal customers become detractors when their previous shopping experience with an e-commerce provider is not good. They are more likely to switch to other competitors.

Predictive marketing will analyze these data minutely and check for the number of complaints the customer made in the past six months to one year and predict the trend. If the old issues are resolved, and credits or vouchers are issued to a loyal customer, it is highly likely to bring them back again.

2.     Using a Robust E-Commerce CRM Software-

A state-of-the-art predictive marketing CRM system will ensure data protection, security and accessibility, and will alert the admins for any kind of potential breaches. This CRM software will give you the right kind of monitoring capabilities and control of your data. You, being the administrator, will have complete control over the transactions and data sets. You will assign the relevant access to your users as per their levels and once a user exits, you will have complete control to disable their accounts.

It will authenticate the right kind of user and will protect the data of your customers from hackers. You can audit the data of the customer logins and the predictive model will immediately block any potential threat.

3.     Predictive Email Campaigns

When you run an advertisement for a new fashion line launch listing on various channels like YouTube, email campaigns, robotic calls, banners, flyers, etc., and multiple customers respond to that, you need to manage your mailbox well. You need to manually sit and update the leads in your database, follow up with existing customers, etc. Often this kind of manual work takes a lot of your productive time and you might lose out on prospective buyers to your rival e-commerce portals.

Your sales team will receive emails from various sources like lead magnets, online forms, social media platforms, walk-ins, events, contact center queries, etc. The predictive email campaign automation software can help to set up a drip campaign and send relevant e-mails to the target audience.

Predictive Email Campaigns

4.   Predictive Product Recommendations

Accenture Pulse Survey 2018 came up with a great revelation. It found that 91% of online shoppers prefer the e-commerce brands that treat them as an individual and do not believe in a ''one-size fits all'' approach. They tailor-make recommendations based on their likings and remember their personal preferences. These predictive models come up with valid recommendations which are relevant to them.

The following categories can be useful for a regular e-commerce fashion brand shopper.

  • Main Page Recommendations- Should include the popular products, new arrivals, rating-based recommendations, etc.
  • Product Page Recommendations- Should include similar products, customers who bought the apparel which they are planning to buy, viewed/bought this, etc.
  • Cart Recommendations- Should include matching accessories, frequently bought together items.
  • Search Engine Results Recommendations- An e-commerce portal needs to analyze the keywords which customers normally use to search for a product. They are not aware of the actual product name which you have listed on the e-commerce portal. They might search with keywords like, ‘’The saree which Kareena Kapoor wore in this particular movie,’’ etc., and if they get 404 not found results, they quickly shift to another portal. 

5.     Customer Churn Prediction

Getting a new customer to buy from your portal is difficult. Hence all successful companies depend on repeat business. However, often customers silently churn out and go to another portal. Companies which let this happen will never be successful.

The predictive marketing tools should be able to identify the data of those customers who were regular buyers till a particular period and then suddenly vanished. Email campaigns should be designed just to target such customers, and if they still do not respond, it will be a good idea for a brand manager to call them and find out what went wrong. Most of the loyal customers will talk about their bad experiences.

Customer Churn Prediction

6.     Lucrative Wallet Loading Offers

Many e-commerce companies come up with offers like if you load the wallet with Rs. 5000, you can shop for goods worth Rs. 6000. These are loyalty programs offered by e-commerce portals. The predictive marketing applications should be able to identify frequent buyers and come up with such offers exclusively for them. 

Lucrative Wallet Loading Offers


With these tips on how to use predictive marketing, your brand is bound to grow. Connect with Fashinza to simplify your manufacturing. We can help you to meet the right kind of cloth manufacturers and source the right materials for your fashion brand.


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