We had a chance to speak with Areef Ahamad, Senior Manager of Analytics at Luxottica Retail. Areef is an analytics manager with hands-on experience in Adobe and Tealium implementations, technical integrations, data analysis, online marketing, RFP process, contract negotiation, consultant management and programming. We chatted with Areef about standardization, the nuances of training new analysts, and how to approach analytics tools when you’re starting from scratch.
Key Learnings:
1. Standardizing reports, consultants and technology will drastically streamline your SWOT analysis process.
2. Three months is the minimum amount of training time to allow new analysts to learn enough before they’re ready to dig into the data and come up with some actionable insights.
3. There can be two parts to analysis. The first is solving a problem or a question someone might have regarding some data that doesn’t look right. The second type is where you don’t know the question yet; you are just getting into the data and trying to see if there are any trends or if there are underlying patterns.
4. Segmentation data is really necessary; otherwise, you are just looking at the overall picture. To get an insight, you really have to slice the data in one way or another.
5. During presentations, keep your analysis short. Mention the actionable insight and show how much money you can make or save by implementing action around it. Don’t go into details; just show the results.
6. To avoid becoming overwhelmed with data, ask management very specific questions about which metrics to track.
7. If you’re starting from scratch with analytics tools, find the tool that’s easiest to use. It takes a lot of time to learn new things and to get results from a tool, which can be frustrating and cause teams to quit using it altogether. Some companies spend hundreds and thousands of dollars on tools without getting anything out of them because of the steep learning curve; it’s like giving a beginner driver a Ferrari.
8. Have realistic expectations, find people who can learn things pretty quickly and implement analytics tools slowly. Go with basic tools first, and once you are at a stage where you’re ready for more capabilities, then you can upgrade. Usually, management says that it’s a waste of time to buy a smaller tool now and get a more advanced one in six months, but it actually takes about a year to become fully comfortable with a tool.
9. Having a good consulting network is essential to supplement the expertise within the company itself. It’s also helpful to go to conferences and training classes.
Interview:
ML: Thanks for speaking with us today, Areef. So, you have been in the field of analytics for quite some time now. What do you still love about your job? What gets you excited in the morning?
AA: I’ve always been a numbers person. My background is in engineering, and I’ve never been very creative. I started off as a programmer and then moved into e-commerce. And when I was in e-commerce, the numbers spoke to me more than the strategies on the creative side, so I did e-commerce for about five years and then programming for five years. I eventually decided to go into analytics, and I’ve been doing that for about four years. Analytics was always a part of my other jobs, but now I do it every day, and I love it because numbers and technology continue to fascinate me.
ML: Fantastic. How would you define the job of an analyst today?
AA: It can mean anything. Analytics jobs can be divided into two parts. The first analyst is the person who gets the numbers into the database or a tool, like Adobe. The second person then picks up from there and does the analysis using Excel or other tools. So, there is a clear distinction between these two jobs. One person does all the technology, worries about the JavaScript, HTML, emails, etc. This technical person then gets the numbers into the hands of an analyst. I think people usually have their hand in a bit of both, and some gravitate more toward one over the other. I have been on both sides, but I lean heavily on the technology side.
ML: You are responsible for analytics reports across a multitude of sites. How do you work efficiently on large-scale companies like Luxottica, for example? I can imagine that SWOT analysis is more difficult when you are responsible for dozens of websites.
AA: It is always a balancing act, and there are never enough people. One of the things we do here is to try and standardize. With Luxottica, I deal with five different brands, so if each brand wants things tailored to exactly their needs, you would require five different people to tackle those projects. So, we standardize by building out one solution requirement and we try to switch all the five businesses into that solution requirement. Alternatively, we have one consultant work on all five of the brands. Standardizing technology helps, too. Previously, different brands used different technologies, but now we try to get them all on the same page.
ML: So, you standardize your reports, consultants and then technology. Perfect. Moving on, analytics and data have been important for organizations for decades. However, over the past several years, everyone has been jumping onto the web analytics bandwagon and saying how important it is. In your opinion, why has there been such a spike in this industry? Why are so many organizations dying to invest?
AA: It had to happen some time. I think it all started with e-commerce. Data needs used to be very simple. You did some marketing campaigns and you sent out catalogues. The market had been the same for 50 years, so the analytics didn’t vary too much. But with the start of e-commerce in 1990, things started to shift. It’s such a data-rich channel, and when e-commerce started, I think people were very busy trying to get the technology right. It took 10 or 15 years for that revolution to happen. And now that e-commerce is pretty established in its current form, I think now is the time to start focusing on the data. Using your data in the right way can drive business, and people are just now starting to understand big data and how it can do all these wonderful things. That boom of interest is only going to get bigger.
ML: So, because of that boom, a lot of organizations are hiring web analytics talent. When a person is starting an analytics job, what should they do in their first 90 days?
AA: Firstly, it’s really hard to find good analysts now. A good analyst really needs to understand the business. They might be someone in finance, mathematics, engineering or marketing, because they pick up things easily. Right off the bat, a new analyst should understand the business and the data requirements. So, that would be the first month. Once they have a handle on the business and understand the data requirements, I would recommend that they undergo some kind of analytics training. They should allow about a month for that information to sink in. And in the third month, they should start developing basic reports, and it will take another month to understand those reports. I think that three months is the minimum amount of training time to allow them to dig into the data and come up with some actionable insights.
ML: How do you go through the process of analysis for use in a presentation?
AA: There can be two parts to analysis. The first is solving a problem or a question someone might have regarding some data that doesn’t look right. That part of analysis is pretty simple. The second type of analysis is where you don’t know the question yet; you are just getting into the data and trying to see if there are any trends or if there are underlying patterns. So these are the two things that i get into.
ML: And what type of analytics do you generally do?
AA: My time is mostly spent on technical work, but when I get into analytics, it is either marketing analysis or doing analysis on specific campaign results where we are trying to find out which campaign produced the most revenue. We also analyze consumer behavior on websites. Adobe can tell us what happened, but it can’t tell us why it happened. So, we use other types of data tools to ask the “why” question. We also analyze the customer’s experience data to see where people are clicking. We combine all this data to get an understanding of user experience and to do attribution analysis.
ML: Do you have any favorite reports or dashboards that you use? Do you have any particular segments that you look out for? It would be great to hear your recommendations on things for people to pay attention to and/or analyze.
AA: I think segmentation data is really necessary; otherwise, you are just looking at the overall picture. To get an insight, you really have to slice the data in one way or another. One thing that we do regularly is to segment the data by biased vs. non-biased. For example, we’ll segment first-time buyers vs. repeat buyers. Conversion rates are very different between the two. For every analysis, there is always some type of segment to compare.
ML: So, the analysis is done and it’s time for you to present your findings. How do you keep your presentation not just interesting, but actionable? A lot of people share information that seems compelling, but they might not lay out the next steps. How do you make sure that people follow through on the desired actions based on the analysis?
AA: This is a very important and difficult step. We understand what the actionable insight is, but to convey that information is a challenge, because we are talking to a higher-up or someone who is not super familiar with analytics. I would keep your analysis short. Just mention the actionable insight, show how much money you can make or save by implementing action around it. Explain why you need to do it and what it will cost to do it. Keep it at that level. Don’t go into details; just show the results. As analysts, we pride ourselves in showing all the details of how we arrived at the solution, but the higher-ups are not ready to listen to all that, so we keep our presentations really short. We explain the insights and the benefits but the process itself is in a separate file. We only share that if people are interested in the details. Usually, people don’t want details; they just focus on the problem and ask for few options for how to solve that problem.
ML: Very fair. To build on that, everyone is currently overwhelmed with data. I’ve heard many people say that they are drowning in the data that is available. Do you think that this is the case, or do you think that many people just haven’t found the right information that really matters?
AA: There is just so much data; we can track everything with e-commerce, so it is so easy to get stuck in this data ocean. What usually happens is that the analytics department asks management what to track, and management says to just track everything, and they’ll look into it later. I think people go wrong at this first step. It is hard to convince people to take a different approach, but I find it easier to get there by asking very specific questions. First, get the business requirements in writing and develop your reports just for those business requirements. You can stray a bit here and there, but try to develop the reports solely around those requirements. State that these are the most important questions to answer, and leave the option open to investigate more specific data if needed. When we take that very targeted approach, people will ask the right questions and get the answers they really need.
ML: Wonderful! So, let’s shift gears a little bit and talk about technology stats. When a company is considering a new analytical platform, what should they look for before making a purchasing decision?
AA: Good question. The best approach is to look inside your own company first to see what tools you already have. Recently, most tools have become standardized. Many tools do the same thing, but they have different ways of doing it. If you’re starting from scratch, I think the best way to go about it is to find the tool that’s easiest to use. You might compromise on certain features, but if you’re starting from the bottom, I would go with Google because it’s easy to learn and get things started. It takes a lot of time to learn new things and get results from a tool, which can be frustrating and cause teams to quit using it altogether. Some companies spend hundreds and thousands of dollars on tools without getting anything out of them because of the steep learning curve; it’s like giving a beginner driver a Ferrari. I would rather buy a Honda and work my way up to that Ferrari once I know how to drive.
ML: How do you stay current on all the changes and new technologies that are taking place in the analytical and digital world? You mentioned the analogy of buying a Ferrari and not knowing how to drive it, but how do analysts decide which tools to add to their technology stack when they have almost endless options? What should they “keep in the garage,” if you will?
AA: So, what’s happened is that people keep all these expensive vehicles in their garages, but they don’t have anyone who know how to drive them. They’re driving these expensive, powerful vehicles at five miles per hour. There are so many technologies out there, but hiring the right people to use them is so important. Have realistic expectations, find people who can learn things pretty quickly and implement the tools slowly. I’ve seen companies become very ambitious; they listen to the salesman and sign on for a solution without considering the time it takes to learn and implement it. So, I would ask for companies to go slow. Go with basic tools first, and once you are at a stage where you’re ready for more capabilities, then you can upgrade. Usually, management says that it’s a waste of time to buy a smaller tool now and get a more advanced one in six months, but, in my experience, it actually takes about a year to become fully comfortable with a tool.
ML: Wonderful. So, what does your analytics technology stack look like? I know that you use Adobe analytics, and I’ve heard you say that you’d love to start using Google Analytics Premium, but what are some other tools you’re using for data visualization or data warehousing?
AA: Yes, we do use Adobe reporting and analytics as our basic tools, and we use Tealium for tag management. We use a few other tools, like OpinionLab for surveys, and we use Clicktale and Lucky Orange for CEM. We also use IBM’s Unica.
ML: Are there any new point solutions that you are interested in?
AA: Not right now. Our new initiatives are focused on personalization, so we’re using Monetate and audience stream for that. Otherwise, we are not planning to use any new tools in the near future. We are planning to use more features from our existing tools.
ML: So, you’ve been in this field for four years and have tremendous experience working with some other fantastic sites. As experienced as you are, where do you go to find answers to your own questions?
AA: Oh, I have questions all the time. I used to think e-commerce was too complicated, and I thought narrowing my focus to analytics would help me get more answers. But that’s not the case. Each tool takes a huge amount of time to study by itself; you need at least five brains to learn them all. And when I see jobs advertised where companies want people to have expertise in all these areas, it’s just not possible. I don’t have five brains; I have one brain, and it’s getting old, so I have questions all the time. So, when I have questions, I go to a few different places. For questions about Adobe, we go directly to their people. We also rely quite a bit on our consultant, who has access to experts in each given field to help them out, and they put their brains together and come back to us with their answers. Consultants are a huge pillar for the success of any company. Having a good consulting network is essential to supplement the expertise within the company itself. It’s also helpful to go to conferences and training classes. Conferences are good for getting a general idea on certain topics, and training classes are for more in-depth study.
ML: Any conferences or trainings that you might recommend?
AA: Yes, I highly recommend the conferences that are put on by your own vendors. So, for me, it’s always the Adobe conference, the Tealium conference and the conference that our consulting company puts on. I get a lot of targeted information from these resources.
ML: What do you wish you had known about web analytics when you were first getting started in this field?
AA: Good question. I wish there was a more organized way of learning. I think that’s where a lot of people have struggled, myself included. That there is no proper university course, and there are no proper manuals. So, you learn everything by doing and by reading articles from the web. It’s all random. For all the other things that I have done, there were specific courses and books that provided a foundation, but it’s all been on-the-job learning for analytics. I just wish it had a clearer path.
Resources:
You can connect with Areef on LinkedIn
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