The past week i was pushed into data analytics and somehow i rolled up to find the best analysis to do with B2B.
Most companies in the B2B space are using web analytics to track the basics; page views, visitors, bounce rate, and similar metrics. Today, that is the low bar. The true value that can be extracted from the way in which customers interact with a site isn’t lurking in an obscure Google Analytics report. It’s buried in the wealth of data that can be collected from customer interactions. For companies both small and large, the world of data science can be intimidating and knowing where to start can leave one’s head spinning. But ignore this at your own peril. OK, so a full discussion of data science is beyond what’s possible in a blog, but here are some key points that all in the B2B space should know:
Customer classification :Understanding behavioral tendencies of customers and grouping them with similar customers can be an effective way to focus marketing and merchandising efforts. One classification that most have likely seen is the “VIP Customer” but too often, B2B companies simply decide, using a single metric, what constitutes a VIP. One implementation I’ve seen simply classifies any customer that purchased over $100 as a VIP and placed a little badge on the customer’s online profile. That’s not really classification; it’s closer to gamification which gives the customer some sort of sense of achievement and hopefully boosts their loyalty. Real classification is based on a set of data features measured across all recent customer activity. In other words, whether someone is a real VIP customer or not, is going to change when compared to the behavior of the entire customer base. Knowing customer classification and being able to use a classification model to predict a group to which a new customer is likely to belong, can help greatly in determining which promotions to show to a customer while they’re on the site or via email. In a recent machine learning implementation we did for a B2B firm, we grouped customers into several segments by using data from a recent time period:
It’s also important to note that models aren’t static. They must be recalculated frequently. In the recent implementation, the models would recalculate every few hours to ensure that they were as current and as accurate as possible. Prescriptive Product and Content Placement :There are many reasons buying behavior may change. Seasonality is one such reason. Other reasons that may drive short term behavioral changes are weather, news, shortages, and manufacturer promotions. Using models to detect conditions that are “outside of expectations” and adjust site merchandising to respond to time sensitive anomalies are becoming more commonplace. This technique can be used to alter search results, home page item placement, category item placement, and guide users to product information when a search for a competitive product is conducted. Does a given product perform better when visible at the top of the home page? Some do, some don’t. Being able to detect optimal placement for products given a recent history of activity such as conversions and cart additions can be accomplished through machine learning. When introducing a new product, similar models can be used to predict a best-placement for optimal performance based on product attributes. Improving Personalization :Personalization on most B2C sites has a long way to go. And on B2B sites, it’s still in its infancy. While it’s possible to group shoppers into a cohort and show them similar items, machine learning makes it’s possible to make these cohorts smaller, approaching a more unique experience for your B2B customer. This can be especially impactful when there are multiple B2B buyers from a single customer. To begin moving toward a more unique experience, machine learning models can be developed starting at a high-level, then progressing to more granular levels that deliver unique insights into a customer. For example, beginning with geolocation as a factor, a B2B seller of industrial HVAC equipment should feature different products for customers in Minnesota vs. those in Florida. The buying seasons are not only different, but events in Florida such as an impending hurricane may influence buying behavior in the short term. A properly designed model can help spot these changes and alert the B2B marketer to changes that may require a change in site merchandising for a geographical region. To add on, a company could develop models that can predict the optimal sort order for search results for a customer, the ideal products and categories to feature for them, along with suggested promotions based on their recent behavior. In Summary :It’s a fascinating time to be working in commerce and with data in particular. The convergence of low-cost cloud-based computing and the abundance of data available from a wealth of sources (not the least of which is a company’s B2B site) and provide a lot of actionable intelligence with an investment orders of magnitude lower than a decade ago. That not only puts this technology within reach, it positions it to become a core part of how you relate to your customers and how you operate your business. Those that ignore the call today, may likely be tomorrow’s Borders. Hello all there !!!!
I've just want to present my new work on scrapping. This is not too late for the scrapping, the modern age mechanism to steal data. When i started to do so : I always have high personal context awareness, when stealing from websites like amazon you need to take more care on your privacy. Things i gone through while started scraping is :
They are clever, Really clever. We do wanna show us clever. There exist several proxies in the world and change IP like flying from Pakistan to America in second. ("Not even Superman can"). Sounds Great :) BREAK THE FIREWALL - AMAZON:
2. Strip "tracking" query params from the URLs to remove identifiers linking requests together
Decided to dive deeper into Python - My code for achieving this. I don't wanna explain my code here, I already gave enough definition in code. follow the steps and be careful about your privacy. This tutorial is completely for Education purpose. Scrapping is Illegal and i don't promote it. Thank you - SHREE THAANU |
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Photo used under Creative Commons from nan palmero