Posts tagged "Web Analytics"

clickstream data

Unlocking the Value In Your Clickstream Data

November 15th, 2017 Posted by Behavioral Data 0 thoughts on “Unlocking the Value In Your Clickstream Data”

In 2017, 65% of all US retail sales are involving a visit to the company’s website. And as you may have already realised, your website is one of the most important sources of data when it comes to understanding your customers’ wants, needs, and preferences. Assuming you have web analytics, this source of data gives you the opportunity to track and collect data on every single interaction a customer makes with your brand.

In-store you can track what a customer has bought or returned. On social media, you can get a view of which adverts they have seen, or which content they have browsed. But it’s only with web analytics that you can truly track every interaction, regardless if a purchase was completed.

Sure, you cannot track the mood the person was in when browsing nor what they had for breakfast. But no other channel gives you the chance to learn so much about your customers. From which pages and products someone has viewed to time spent on page and engagement with bespoke content widgets. From basket interactions to video plays and customer feedback views. It’s clickstream data capture which allows you to track every interaction with your brand.

The aforementioned clickstream audience gets us to a point where most organisations are today, leveraging their clickstream data in their chosen personalisation, DMP, and email tools. There is, however, much more that can be done with this data. You might say that you could look at a customer’s historical in-store transactions and work out what you think they might be interested in next. However, finding a segment of 100,000 people, in the last 14 days, who have viewed a certain product type without purchasing, is undeniably more powerful. I know which audience I would rather market to.

So, if 65% of all US retail sales are involving a visit to the company’s website, and if overall web share of sales typically ranges between 35% – 60%, there is no doubt about the impact your website on overall sales.

e-commerce sales increasing

How can we unlock the value of our clickstream data?

Web analytics is great. You can track demand, product interest, online sales, and even identify problem areas on your digital properties. These are just some of the critical functions your web analytics tool can do. However, such tools have their limitations.

The type of querying you can do is pretty limited, as are the types of visualisations you can create. The data science functionality, when available, is restricted to proprietary, pre-defined models. The schemas that these datasets are stored in are predefined and the type of data you can pull into these applications is generally limited to lookup data.

To really unlock the value in your clickstream data you only need to pull the data out of these applications and into a Big Data platform. When you do this, you can knock down the barriers of restrictive querying, focus in on individual customers (not cookies), and get clickstream data feeding in along with your many other data sources. Only then can you truly achieve an omni-channel view of your customer.

The 5 steps to unlocking the value in your clickstream data are:

1. Customers buy from brands, cookies do not buy from channels. Do Big Data.

Most organisations have realised that customers expect an omni-channel experience and have started (or are about to embark on) a Big Data platform project to handle this. It is of paramount importance that the clickstream data be a part of this project. Clickstream data can be tricky to work with, as Web Analytics vendors do not always provide this data in a format that is easy to use. However, with so much data on existing customers, as well as prospects, this data source is just too valuable to ignore. When the clickstream data is accurate and made available in your Big Data Platform, in a schema that makes sense, you can achieve a true, omni-channel view of how your customer is interacting view your brand and begin to understand and model how best to serve them.

clickstream data

2. Data Science. Stop ignoring 90% of your data.

When querying and segmenting, web analysts tend to focus on high-value data such as purchases, cart adds, visits to key pages in the conversion funnel, etc. You might then segment this data by some key dimensions, product types, geographical properties, first-time visitors, etc. Realistically you might be able to take these key metrics and apply 5 or 6 layers of segmentation to get to something interesting, be it an audience or a metric. Even in this scenario, you have probably ignored 90% of the data you have for each visitor included in the audience. Think of all of that data your Web Analytics tool collects with each tag fire. Then multiply this by the number of tag fires each visitor generates. You have so much information on your web visitors; why ignore it? Using data science techniques, you can begin to consider the other 90% of your clickstream data and begin to find behavioural patterns. For example, just because I have viewed a certain product or service more than 3 times, or added something to my basket before abandoning, does not mean I am ripe for retargeting. Conversely, just because I have not completed these interactions does not mean I am not interested in buying the product. The truth may lie in everything else I have done on your website. Start considering the full picture and use open source libraries when doing so.

3. Reporting, it is a business-wide problem.

Every business needs to produce reporting that tracks sales, revenue, and demand. Clickstream data is a vital component of this, particularly for tracking digital sales but perhaps most importantly as a measure of demand for your various products and services. If you do not have your clickstream data in your Big Data Platform you are left with the unenviable task of manually trying to combine separate reports into a single sales dashboard. Worse still, you may simply ignore the clickstream data and not report on your demand levels. But with your data consolidated in one place, not only can you easily produce high-value omni-channel business reports but you can also be more agile in answering any follow-up questions, with the required data in one place and ready to use.

4. Monetise your clickstream data, it is already costing you a small fortune.

Web Analytics tools are not cheap, and the more data you collect the more you have to pay. My previous points have examined optimal ways of storing, processing and querying your data, allowing you to learn more from the data you already have. Once you have accomplished this and created various scores and segments for your existing customers and prospects, you need to get this information into the applications you use to communicate with your customers. Connections between your Big Data Platform and your marketing tools (DMP, personalisation, customer contacts, and anything else you use), be it for online or offline customer communication, need to be established. At that point, you can automate the data flows and ensure customers are receiving a relevant, personalised and consistent experience with your brand, across the different channels that are available to them.

5. Automate everything you can.

Within an Analytics function, there are a limited number of people. Typically, these people are highly skilled in one or more areas of Analytics, be it querying, data science, visualisations, or data engineering. By automating as many processes as possible, you can ensure that the resources available to you are delivering new insights and are seen as a value driver rather than a potential constraint or blocker. When it comes to clickstream data, the aim is to automate the ingestion, processing, and auditing of this data in your Big Data Platform. This frees up the data engineer to work on new data sources and ensures your analysts and data scientists have up-to-date, ready-to-use data at all times. The same then applies to getting data out of your Big Data Platform and into the applications you use to communicate with your customers. Establish these data pipelines and automate these flows. Without this, you risk becoming a blocker to the business, as the time to delivery of new outbound data sends will be very long.

So, if you’re looking to boost sales and revenue, clickstream data will be your go-to source of data to find the customers to reach out to. It should also be an essential component in your businesses data strategy. If you get this data into your Big Data Platform, in a structured and usable format, and establish the required outbound data pipes, you will be able to give your analysts the tools they need to increase data value, and your marketing team will be able to maintain true, omni-channel engagement with prospects and customers.

 

References:
     – Digital Commerce 360: 60% of U.S. Retail Sales Will Involve the Web By 2017
     – Deloitte: Understanding Consumer Shopping Behavior
     – RetailNext: Brick & Mortar Vs. Online

 

 


Bal Basra

Bal Basra is a Solutions Consultant at Syntasa, assisting customers and prospects in extracting the maximum value from their big data projects. He brings tremendous knowledge of analytics products and methodologies from previous positions at Adobe and TUI. Connect with him on LinkedIn.

 

dynamic analytics

Dynamic Analytics Are the Way of the Future

October 12th, 2016 Posted by Analytics 0 thoughts on “Dynamic Analytics Are the Way of the Future”

Is there such a thing as too much data? Modern businesses are finding themselves sitting on top of a treasure trove of business intelligence, from web analytics to internal figures and customer logs. There are several different ways to exploit this data for different outcomes.

As we wrote last week, the three main types of analytics tools available to businesses today are web/app analytics, enterprise business intelligence and digital analytics, and Predictive Behavioral Analytics. With web analytics, businesses can evaluate the success of their websites, computed in page views, clicks, unique visitor numbers and purchase amounts. They can also gauge their best sources of traffic based on referral sources, and verify which pages are the most effective at keeping customers engaged, with bounce page data. All this information provides great insight into how a business is faring online and allows businesses to tweak their websites/apps in order to draw and retain more customers by improving customer experience.

Enterprise business intelligence operates similarly to web analytics. It helps businesses make smart decisions based on the data available to them, but the focus this time is internal. There is an abundant amount of metrics that describe how a company operates (sales forecasts, revenue/cost data, inventory levels, project progress logs, HR data, and much more). All this data, processed through the right analytics system, can help business leaders quickly identify where the inefficiencies lie. A judicious use of enterprise business intelligence will allow business leaders to ensure they run a tight ship.

But both web analytics and enterprise business intelligence are static. That is, they allow the business to exploit the data only after the fact – that is, once the customer has made their purchase online and internal business decisions have been made. In an era where data is an instantaneous, fluid source of essential business information, such a lag is not only inefficient and costly, but also fully preventable.

That’s where dynamic analytics comes in. It analyzes a customer’s behavior and responds by creating a bespoke user experience as-it-happens, thus ensuring an optimal sales outcome each and every time. The data trail left behind by each single customer interaction is also fed back into the program, making the dynamic modeling even sharper the next time around.

Three Types of Analytics to Leverage Big Data

October 6th, 2016 Posted by Analytics 0 thoughts on “Three Types of Analytics to Leverage Big Data”

In the era of big data, companies are finding more and more ways to fine-tune product offerings for their customers. There are several layers of analytics to glean insights from, chief among them are: web analytics, enterprise BI, and behavioral analytics.

With web or app analytics, a company will gather data on clicks and page views, as well as a number of other metrics that indicate what kind of traffic the website is attracting. These include the number of unique visitors, downloads, referral sources, and page bounces. Using this data, the company can, for instance, aggregate data on the most popular topics and pages on its website. Such data serves as the blueprint for a website that is easy and pleasant for customers to navigate. And like a judicious accountant, web analytics allows firms to get a clearer view of what is driving sales performance.

But companies nowadays also have the opportunity to go even deeper. With enterprise business intelligence, they can turn the lens back on themselves to reveal any inefficiencies within. There is a dizzying array of internal data at their fingertips, from sales forecasts to inventory levels and revenue or cost data. Slicing and dicing this data with flexible reporting and ETL processes allows businesses to learn valuable things about themselves. Enterprise business intelligence is like the eagle-eyed consultant who swoops in to identify how the firm could improve.

Big data has also given rise to an entirely new dimension of data analytics based on the real-time processing of customer patterns. Companies can observe customer behavior as it happens and respond instantly through dynamic webpages. The company’s own data on past customer journeys (where users go and what they end up doing) can help to create different types of behavioral profiles, called customer segments. Once the website learns to recognize a hesitant buyer, it can respond accordingly, such as directing the customer to a discount offer.

We call this form of analytics Predictive Behavioral Analytics and it allows companies to offer a user experience that is as personal as interacting with an affable customer service representative. It’s like having Don Draper assist every single customer that passes through your website.

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