It’s a proud moment for all of us here at Convergytics. The phenomenal growth that we have seen in the past 9-12 months has finally gotten some recognition. A month OR so back, we were asked by GBM – A UK Based Branding Magazine to fill up a form to be in consideration for the Prize in the category of Analytics Startups in Asia. We filled up the details and didn’t expect much. A little over a week before the event on December 15th, we were pleasantly surprised to hear that we had been declared the “Fastest Growing Analytics Startup” in Asia. This has only spurred us to work harder in pushing our story & committed us to our core value-proposition & continue to deliver value to our clients. Thanks to the Team, Clients, Support Teams and the families who stood by us. Below is a sneak peak of the award that Sanjeev and Randhir collected on our behalf. See photos on the Convergytics Facebook page The Award: here The press-release from Global Brands Magazine – one of UK’s leading Branding Magazine.
Analytics Project Tips
John (name changed) is a Marketer for a leading apparel eTailer based in New York. John joined his current company recently and is struggling to make sense of the reports in his Inbox. The #s and findings in the reports just don’t add up. While their analytics vendor sends out 10-15 reports each week, no one on his team uses these reports. No one on the team was even aware of who requested so many reports. The vendor blames the poor data quality for the unreliable numbers and the poor usage. John wonders how he can improve the quality of his analytics projects and if there is a solution.
This is a typical scenario encountered by many a marketer / e-marketer. The root-cause of these are common mistakes that analytics teams (internal and external) make. What are these common but critical mistakes and how can you make sure you avoid them?
1. Unused Reports / Unimplemented Models = No Perceived Value
Is your team / vendor doing work that is eventually not getting used?
You should first inventory the analytics projects and identify the key stakeholders for each. Identify team members who will benefit most from each project. Get these team members to review the work and use it to make decisions. Motivate them to inform you if they feel that they may not have a need for it (now rather than later).
You should do a quarterly exercise to cut redundant work by sitting with your team and your vendors . This exercise will ensure only useful projects remain. A review before a major project launch will ensure you are investing only in useful projects.
As a vendor, you need to take ownership of tracking usage of each project that you work on. Most BI Tools today come with usage tracking features. XL and other reports sent using email can use email/link tracking tools to measure usage. Share the findings with your project sponsor to showcase the impact of your work. A dramatic solution to an unused report is to pull the plug on it for 1-2 weeks and see if anybody complains. If no one does, you can indeed pull the plug on those reports.
2. Erroneous Work = No Credibility
Is your team following due procedure when it comes to QA of the reports / analytics projects that are going out?
Are there a standard set of checks required before any report / analytics recommendation / model goes out? Is the process documentation detailed enough to avoid errors due to personnel changes? Are there many QA check-points within a project? E.g. Ensure that the # of customers in your modeling data-set on is in the ball park.
QA is an important ingredient for an error-free deliverable in any analytics project. You need to confirm advanced analytics projects and presentations make business sense. You also need to ensure that the model is a good model.
3. Data Discrepancy / Data Quality = Unreliable Deliverable
Who do you blame as an analytics team for poor data?
The Data Team (if you were not part of it) or just the stars (if you were). Data will never be perfect. You need to budget enough time in a project for data treatment and call out the assumptions made.
When many data sources are available, you must use the newer, more reliable and less error prone one. It could also so happen that the data was clean for some channels and no so clean for some other channels (e.g. Online). In that case, adopting a hybrid strategy might work better. A first step is to gain access to all key data sources and compare the key metrics (KPIs) across them.
A digital website audit by a digital analytics vendor would be a good first step. This would help identify mistakes around usability, analytics implementation and reporting. You must repeat this audit at least 2-4 times a year to ensure any new changes made do not impact the site and the data. You might need more frequent audits for a website that undergoes frequent changes.
4. Conflicting Stories = Poor Credibility
Does your report tell a story, a single story?
Getting a deliverable ready to present / share with your client is only the first step. Making sure that the #s and recommendations make sense is important. It is easy to get lost in the numbers and lose sight of the reason for the request. The “So-what” or the “5-Whys” technique will help get to the bottom of what the numbers mean. They will also help make recommendations to the client to take certain actions.
You must remember “correlation is not causality” and “averages are misleading” when making recommendations. You might need to drill-down further to see if the recommendations you are making are valid.
5. Projects that don’t drive decisions / actions = No Value
Is your project making an impact?
You feel that you are doing some great work. But, is it making an impact by driving decisions. Is your model implemented in market? If your answer is “No”, you might not have given it enough thought at the start of the project. You must invest time at the start of a project to ensure that you are solving the right problem. You must also ensure that you are structuring the problem in the right way. A well-structured problem helps more than a cutting-edge tool or even a sophisticated technique.
You must ensure that any report includes recommendations based on your interpretation of data. You must also encourage users to provide feedback on these recommendations. Starting a conversation intrigues more stakeholders to use and get value from the reports. Even if you get corrected on your findings, it is likely to be because of some information that you missed as a team. You should request for the extra information and use it to improve the recommendations. More information could include campaign calendars, creatives, market dynamics or product launch schedules.
Author Profile: Randhir Hebbar is an entrepreneur and one of the founders of Convergytics – Asia’s leading analytics brand in 2015-16 (as per UK based Global Brands Magazine). He heads the Digital Analytics and BI Practice at Convergytics and also is the Account Lead for several key accounts. He has consulted with dozens of leading Fortune 500 Retail, e-Tail, Technology, Telecom and Media Companies over the past 15 years and as an Analytics Leader within the organizations that he has been a part off, has encouraged team members to strive to avoid some of the mistakes listed in the article.
Posted in Blogs, Tagged analytics project, Convergytics, mistakes in analytics, Web Analytics,
Randhir recently published an eBook on “43 ways to optimize your eCommerce website”. This book reflects the philosophy behind Convergytics’ solutions in the Web Analytics Space. As the customers increase the number of devices and channels they use in making a purchase, it becomes imperative to trace this journey and create a richer data set that stitches all these data sources together and gets actionable insights from this data store. This is a must-read for any eCommerce Leader and points out many new analysis tools / techniques / data sources that can be used easily to make changes on your website and deliver higher conversions and a better user experience.
You can download this page here – https://www.convergytics.net/43-ways-to-optimize
Posted in News, Tagged Convergytics, ebook, Web Analytics,
Bollywood is an industry that has a value of Rs. 138 billion and is expected to reach Rs.1661 billion by 2017 (FICCI-KPMG report). And if this is not impressive enough, the industry has produced 1.6 billion movies so far which is 4 times the number of Hollywood movies produced till date. Indians have grown up with Bollywood (popular name of Indian Film Industry), watching all the emotions on the screen. One way or another, it has rubbed off on us to an extent that the tickets sold in India are 2.6 billion, nearly twice of that sold in Hollywood.
With a turnover of 2.23 Billion USD and overall marketing spend of roughly 50% per film, the movie business in India is one of the highest spenders in marketing promotion. Considering the size of the industry, it becomes imperative to ask- “Is Marketing being done here the right way? Can we improve the efficiency of spends?”
A decade ago, most of this promotion was straightforward with a limited number of channels to spread the marketing message to a less distracted target audience. With the dawn of the social media and ubiquity of smartphones, reaching an attention deficit audience is now the biggest challenge for production houses and their marketing teams. Which channels to focus on? How to customize the message for each channel? How to allocate spends across channels? These are some of the questions that challenge today’s movie business.
The future is NOW
The last decade has not only seen an increase in the diversity of devices on which entertainment is consumed, but also in the profile of the audience that is now getting used to custom content.
As digital devices adorn new avatars (e.g. wearables) it will become important to understand what type of content is optimal for these devices and how to customize the content as per insights from the audience’s digital profile. Netflix has already started profiling audiences to inform content creation. As the launch campaigns for new movies go omni-channel they will enhance their targeting to incorporate these different data streams to generate a custom genre fit.
This article was first published on LinkedIn by Sanjeev Mishra (CEO, Convergytics) and was reposted here with minor modifications.
Posted in Blogs, Marketing, Tagged bollywood movie marketing, Convergytics, entertainment analytics, marketing effectiveness, Web Analytics,