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BigCommerce Integration

7thLite and BigCommerce

Benjamin Lentini avatar
Written by Benjamin Lentini
Updated over a week ago

You can make your BigCommerce store even more useful, flexible and effective by integrating it with 7thLite. In this piece, we discuss how to do it.

Integrating 7thLite and your BigCommerce Store

First, from your BigCommerce store, go to the app store, and install 7thLite. After you’ve connected it to your store, you can log into 7thLite just by clicking on its title from the Marketplace section of your BigCommerce store.

Select the Install button.

A confirmation screen will display. Select Confirm.

Sign up for the 14 day free trial and enter the appropriate data on the screen and select the Sign Up button.

A Data Connections screen will display requesting you to Sync the data. You should first select how you would like 7thLite to capture Orders, either by the Order Date or the Shipment Date.

Select the Sync button on the Navigation Panel choose the first Sync option.

The first time you sync your data, 7thLite will import your sales history and product information. The first sync may take a while depending on the amount of data being loaded. After that, your store data will sync daily with 7thLite. You can also sync on demand.

You can now use 7thLite with all of your sales, inventory and order data!

View OTB and Ordering Recommendations

7thLite will provide an overview of sales forecasting based on the sales history in your store and recommendations for which products to purchase in order to meet customer demand.

Click on “OTB.” There, 7thLite will pull from your points of sale or warehouse locations in your BigCommerce store and determine reorder quantities and dates when variants will run out of stock.

Calculating the Forecasted Sales

7thLite uses your BigCommerce store’s sales history to compute the sales velocity, which is the number of units sold divided by days in stock. When we calculate your forecasted sales, 7thLite will look to reconstruct the historical data based on any stock outs that may have occurred.

Stock Outs = the number of days when a product is out of stock.

7thLite automatically detects when a product is out of stock starting from the moment you connect your store to 7thLite. This data helps to estimate the demand correctly.

Throughout the lifecycle of a Variant, the granularity of forecasting dimensions varies. Some processes need a forecast by product attribute by channel by month, while others may need a forecast by Variant / Color / Size by day.

7thLite utilizes a combination of built-in data analytics and forecasting techniques such as:

  • Time series analysis

  • Trending and causality analysis

  • Rank-order statistics

  • Clustering

  • AI algorithm utilizing Case Based Reasoning (CBR)

Fashion / New Reorder product forecasting cannot rely on its own historical data, because it didn’t exist before. One solution to this issue is Case Based Reasoning (CBR):

  • An experience-based approach to solving new problems by adapting previously successful solutions to similar problems.

  • Addressing memory, learning, planning and problem solving, CBR provides a foundation that can solve problems and adapt to new situations.

7thLite will reconstruct historical sales to take into account any lost sales and add them back into the forecast.

7thLite’s AI and Machine Learning techniques continually analyze all sales and inventory data to update the forecasting models giving the best possible forecast recommendation.

Determining Reorder Quantities

The 7thLite framework will automatically determine whether a Variant should be a Chase item (multiple deliveries) or a One Shot item based on the initial performance of the Variant. Each Variant is analyzed by the 7thLite forecasting engine following this lifecycle.

Once sales data is captured, the 7thLite forecasting engine executes the following steps.

Collect daily sales data > Identify Reorder “winning” styles

  1. Predict demand & reorder quantity > Chase or One-Shot

Multiple algorithms are applied to each Variant forecasting an upper and lower forecast of demand. Each of the forecasting models are then evaluated against the upper and lower demand forecasts and the system will select the “winning” model.

The 7thLite forecasting engine utilizes three (3) models and then determines the best model for the Variant.

1. Prophet Model (+ holiday effect)

2. ARIMA Model (Auto Regressive Integrated Moving Average)

3. ES Model (Exponential Smoothing)

Once the winning model is selected, the system will determine if this Variant is a Chase (multiple delivery) item or a One Shot item.

After 7thLite generates the In-season reorder total quantity, the system collects the sales by Variant/size from the POS Sales. 7thLite will breakdown the Reorder Qty. to size using the sales data by size as a “% to total” for each size.

  1. Aggregate POS sales by size

  2. Generate “% to total” of each size to get a size profile

  3. Apply the “% to total” of each size to the Reorder Qty

  4. Display the Qty by size for each Variant

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