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Thread: Using Data Analytics to Grow

  1. #1
    Join Date
    Jun 2019
    Posts
    3

    Talking Using Data Analytics to Grow

    In 2019, the pace and scale of retail has never been greater.

    With more and more D2C brands sprouting up every week trying to steal market share from your space, have you ever wondered, what are they all doing to grow so quickly?

    Your initial answer might be “customer service” or “venture capital dollars” but that answer is incomplete.

    Want to know the answer?
    The first order of operations (after validating that you have a product or a brand that shoppers buy into) involves laying the analytics infrastructure to start measuring differentiate initiatives.

    Nobody hires 100 customer success reps without first understanding the cost structure and the expected increase in customer lifetime value.
    No venture capital is willing to continuously invest business that can’t quantity the return on expected dollars spent.
    Breakdown of this Guide
    Now that you have a rudimentary appreciation for the value of retail analytics, let’s break down the sections (feel free to skip directly to those sections that are more relevant):

    What is retail analytics?
    Retail analytics is the practice of leveraging data to measure performance, augment decisions and monitor results. In retail, common applications include; measuring the performance of marketing campaigns, monitoring the supply chain, inventory management and merchandising.

    A common end result of analytics is the tracking and monitoring of Metrics and Key Performance Indicators (KPIs) which are then employed as proxies to measure the health of the business.

    Where does the data come from?
    Given the proliferation of technologies today, data can come from many sources. Some include:

    • Daily transactions
    • Transactions
    • Day-to-day Operations
    • Shopper Loyalty Initiatives
    • Product Information
    • Marketing Attribution Tools
    • External data sets (e.g. Weather)


    New technologies have allowed us to augment our analytical prowess through the combination of data sets (e.g. promotions effectiveness derived from Marketing Attribution and Sales Data) to paint a more complete picture before making a decision.

    With the proliferation of sensory (e.g. IoT devices) and the reduction in cost for storage and compute in cloud technologies, it is now easier than ever to collect, store and analyse data.

    The goal post, however, hasn't changed.
    What should be my next action to help me improve my business by either helping me increase sales or decrease costs?

    What can analytics help retailers solve?
    TLDR: NOT everything.

    You were already doing the jobs that needed to be done before you implemented analytics. Analytics is meant to help you measure, analyze and focus your efforts.

    Example 1: Planning merchandising
    Before analytics:
    “Guesses” as to which product would do well.
    (If you had more than 1 sales channel, it would be too complex to manage the differences)
    After analytics:
    To provide a more holistic estimate, use;
    historical data,
    product attributes,
    price elasticity,
    traffic forecast,
    customer acquisition cost and
    marketing budgets

    Example 2: Streamlining shopper engagement
    Before analytics:
    Send email marketing to all 1,000,000,000 shoppers on the newsletter subscriber list until something happens
    After analytics:
    1st: Segment list based on open rates (throw away those that never open)
    2nd: Segment list based on purchasing history
    3rd: Use product affinity analysis to see which products tend to be bought together
    4th: Send emails based on the different purchasing history

    Example 3: Optimising digital marketing dollars spent
    Before analytics:
    I know 50% of my marketing budget is wasted. I just don’t know which 50%.
    After analytics:
    1st: Monitor customer acquisition cost (CAC) across advertising channels
    2nd: Drill down to the Google analytics multi-channel funnel
    3rd: Reallocate budget from channels that are useless to those that are working

    Two buckets
    Running a business is f***ing hard.

    We don’t always have time to search for the next great analytics tool so, when it comes time to invest in one, it usually falls into one or two buckets;

    1. Business is going amazingly well, it is time to optimize
    The car seems to be working, time to wisely invest in the engine.

    How do I turn $1 into $5?

    Common Questions at this point include;

    How do I replicate this success with another product?
    How do I replicate this success with new shoppers?
    How do I optimize my marketing funnel to bring new shoppers?
    What should I do to increase the lifetime value of my loyal shoppers?
    2. Business is going terrible, what should I do?
    The car seems to be breaking down, why?

    How do I turn stem the bleeding?

    Common Questions at this point include;

    How did this all happen?
    Invest Early
    If you fall into the second bucket, I’m sorry to say but it might be too late.

    Running a business is hard.

    Why not spend a little extra time investing in analytics early so that you won’t accidentally screw it up later?

    Here is the reality:

    If you have collected NO data, tracked NOTHING and monitored NOTHING.
    You have no way of improving your business, because you have no idea what was going on (besides that there was a fluctuation in sales during seasonal periods)

  2. #2
    Join Date
    Mar 2019
    Posts
    61
    Such a valuable post. I really like your post because I have learned so many things about data analytics. Thanks for sharing...

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