In the analytics space right now, there is a common core: SQL, Excel, Tableau/some viz tool, and maybe R/Python. Revenue, Retention, CAC, LTV, Conversion Funnels, Segmentation are all baseline concepts.
However, it seems like many jobs are moving towards much more domain specific roles. Sales, product, marketing, support analysts can seem much more effective if they’ve worked in the space before. Why is this the case? Are people becoming less versatile, or are advantages in domain knowledge overwhelming? I will not make a case here, other than we must understand the space before we make judgements in it.
IMO general awareness of analytics breadth makes you more effective as an applicant, collaborator, or administrator in analytics. As an applicant, you can identify what teams are looking for when hiring, and how your experience fits in (or could fit in!). As a collaborator, you can recognize expertise different from your own, and find the people who know more than you do. As an administrator, you can cater org structure or infrastructure to specific analytics needs in an organization. You can also recognize your strengths or gaps around the company.
Let’s get started. There are a ton of domain-specific analysts in tech. The roles below cover the ones I’ve engaged with (it should be most of them):
- Operations – Sales, Marketing, Support, Operations, Business Development/Partnerships
- Finance – Financial (FP&A), Corp Dev
- Product/Engineering – Product, Growth
- Other – HR/People, Legal
Note: I’m not going to go into Data Science because there are a bunch of thought pieces on it already. We are more focused on the entry-mid level analyst. If you care more about data science, here are fun articles such as “analyzing the analyzers” and “applied data science”.
Sales [Operations] Analyst – Improve sales through analytics. This includes: lead scoring (rank who to sell to), comp structures (evaluate performance), and arming sales teams with data to use in pitches. (i.e. OpenTable results in X% increased table saturation and Y% efficiency in POS operations, so lets split the operational improvements)
Marketing Analyst – Monitor and improve the efficiency of marketing efforts. Channel Attribution, SEO, SEM, Targeted channels (i.e. Facebook), billboards, TV, promotions, etc. Also maybe collateral for PR & comms. This potentially requires significant knowledge of the space, various marketing tools, segmentation resources (Nielsen, FB), and much more.
Support Analyst – Customer support operations. Operational efficiency on phones/email/etc., text analysis for support (auto-tagging & auto-responses). Additional tasks will inevitably include investment in help centers, refund policies, channelling feedback to Product. You can make decisions cost to implement vs. savings via fewer refunds, increased LTV/Retention rates, and customer “happiness” via NPS.
Operations Analyst – Operations efficiency for on the ground operations. This is a huge umbrella term for supply chain, warehousing, inventory, worker efficiency, etc. I imagine you learn a ton of this crap in business school & stats classes.
Business Development (BD)/Partnerships Analyst – This involves large strategic partnerships, integrations, deals, co-marketing, etc. Here, you have to think about push and pull in a constantly evolving environment. In Dropbox, deals would include pre-loading in phones and computers. In Uber, this includes credit card & hotel points partnerships. There’s also channel/secondary distribution, all sorts of crazy stuff. In the end, you can answer “is it worth it for us or are we getting screwed?”
Financial (FP&A) Analyst – Commonly referred to as FP&A (Financial Planning and Analysis), financial modelling for a business several years down the road. At a 10,000 ft view, you help identify what levers a company can pull to improve the business. You help constantly increase the sophistication of financial models by adapting to new lines of business and improving granularity of assumptions. I.e, we can update the model to assume growth & retention by region instead of global retention number. Now multiply many variables (satisfaction, conversion, engagement) by many dimensions (gender, age, cohort, previous engagement) and all of a sudden your model gets really, really complicated.
Corporate Development (Corp Dev) Analyst – Corporate development involves M&A and other strategic bets. I can’t speak super well about this, but I imagine most members on Corp Dev are capable analysts involved in scoping opportunities, doing due diligence. (Background in investment banking is probably very relevant here)
Growth Analyst – an intersection of marketing and product (above). Increasing top of the funnel, conversion rates, analyzing AB tests, working with teams to “grow”. Unfortunately, the popularized term “growth hacking” is loathed because it’s the synthesis of two negative stereotypes: growth (i.e. spamming) and hacking (i.e. gaming the system). That being said, there is a lot of art, analytics, and grit required to actually grow your user base, and this is a great place to be.
Product Analyst – Understanding the effectiveness of a product & its key results. This includes conversion funnels, software performance, usability, etc. Modern product analytics focuses heavily on A/B testing, but there is also a lot of work around understanding how users navigate a product, where they fall off, and identifying areas of improvement.
HR/Recruiting (People) Analyst – Analyze performance of internal HR and recruiting. Google helped popularize it as People Analytics. On the HR side, this can include surveys on employee happiness, internal NPS, management ratings, etc. On the recruiting side, you’d identify where you win and lose, ways to improve sourcing, diversity initiatives, etc.
Legal Analyst – I have little experience in this space, though I imagine the word “analyst” is decorative in this intensive field. This is probably similar to economic consulting, which involves “quantifying damages and analyze economic, financial, accounting, and statistical issues in litigation.”
Data Analyst – ??? There are still plenty of jobs hiring for Data Analysts. I actually don’t know what this means anymore, but I imagine these analysts that work on operations or core product. You will often have to interview to find out, and it’ll probably fit in one of the buckets above.
As a chef, you might be the master of soups. But you’ll still want to expand your repertoire with steaks, pies, purees, pasta, sauces, and the list goes on. There are several must-know techniques shared across all cooking, such as baking, boiling, broiling, frying and chopping.
In the end, chefs likely become specialized in areas such as pastry or a specific cuisine. They are still capable of all sorts of cooking but still spike in a specific area. This really isn’t any different in analytics at all. Be an analytics chef.
This is part 2 of an N-part series on Analytics in Tech. Find part one at What is a Data Analyst in Tech?