Impact of Sampling Campaigns

In-store sampling is frequently used as a promotional technique designed primarily to show to the potential customer the product benefits whether they are taste related, performance, etc., hoping that a positive experience will be converted to a product related purchase(s). The sampling campaign acts in most cases at an emotional level, therefore most of the purchases are impulse purchases (not planned), making in most cases the impact of sampling campaigns even harder to quantify than a price promotion.

Frequently the sampling is organised by a specialist company, rather than by the staff of the manufacturer or retailer, but in this case in particular the sampling are performed by the retail members of staff who take this opportunity to engage with the customer and talk about the product.

The chart below shows and example of a five weeks promotional activity for different products, the uptake on product related sales and product RSP (2011 data).

Figure 24 Sales uplift due to sampling and how that relates to product price.

The following assumptions/ conclusions are food related sampling activity in store - the same may not be necessarily applicable for a non-food sampling. In general, the success of the sampling campaign normally depends on the type of the product sampled, meaning the broader appeal the sample is in terms of target audience, higher is the conversion rate of the sampling to purchase. The largest uplift in terms of product is when customers have a preconceived idea that they would not like something but they change their opinion after tasting. This was the case on week 5 where a specific chocolate flavour was tasted and the uplift corresponds to 425%. When sampling something which relates to similar products with different price points (depending on the packaging type, bigger quantity per pack, etc.), the customer will not always opt for the lower price points (week 2 observations), even though the biggest uplifts come from lower price points (weeks 53 and 5).

The sales uplift quantities were converted into the number of samples available to be sampled to breakeven, by deduction of the product cost (sales uplift  extra income  price sample  quantities to sample  quantity per store). This information is interesting in the sense that allows to: (1) identify which

products are more suitable to guarantee successful sampling campaigns, (2) determine the maximum sampling quantities by store to breakeven in terms of costs, (3) plan future sampling campaigns, as a similar exercise can be done to determine if a campaign will be successful or not. The quantity per store

required to breakeven will be a good indicator if a campaign has been successful or not, but it does not show the sample impact in other products sales (harder to measure).

Week w52 w 52 w 53 w 53 w 1 w 2 w 2 w 5

Uplift 185% 114% 154% 312% 227% 97% 13% 425%

Sales uplift 656 1768 256 1272 250 86 141 2276

Related product RSP £3.50 £3.50 £15.00 £3.50 £14.00 £7.50 £2.50 £3.50

Quantity/store 546 1,473 913 1,060 1,561 574 314 3,556

Table 14 Sampling campaign performance indicators.

As a matter of fact, there are sampling campaigns where the final objective is not purely to increase the sales or even breakeven in terms of costs. Sampling is viewed as promotional exercise to first of all to engage with the potential customer and tell the story about that specific product and the store/company in general, increase the footfall (leading to sales on other products), launch a campaign for a new product as it can reduce consumers’ apprehension about buying a new product or introduce them to products they were unfamiliar before. Although the potential benefits of in-store sampling are well known, there are not many literature reviews on the impact of sampling in the supply chain, as companies are usually reluctant to provide information which would enable that promotional technique performance to be assessed, so the impact of the sampling in the supply chain is still quite an unexplored area of research.

Ideally the planned sampling would always go according to plan and no disruption would happen to the SC but, as mentioned previously from observation of Figure 24, the sales uplift resulting from the sampling is quite difficult to determine beforehand. In a sampling scenario, when the planned rate-of-sales is not achieved, two opposite extreme situations may happen:

Out-of-stock – when the planned sampling performance is over the achieved sales rate, causing sale losses, included the amount of time between ordering a product and receiving it and the consequent disruptions between sales, order, receipts, restocking, manufacturing, etc.

Over-of-stock – when the planned sampling performance is under the achieved sales rate, causing excess of

stock, which it is mostly located in the retailer agent, as stock was allocated to cover the increase in demand. In this scenario, depending on the quantity leftover after the sampling and the product shelf life, future manufacturing orders are cancelled and stock may be recalled from stores to feed other channels, leading to further expenses in managing this situation throughout the SC. If the shelf life of the product does not support this, normally the retailer discounts the product to increase sales rate and deplete that way the excess of stock.

Whatever the scenario might be, it is essential that the supply chain is prepared to deal with these two extreme realities and there is a contingency plan that mitigates the risks of a sampling campaign. It is fundamental that the stock is in right place so the sampling campaigns need to be perfectly planned and communicated to all the elements of the supply chain (internal or external), so that everyone understands the actions required to minimise these risks. Depending on the period that the sampling campaign lasts for the following approaches are possible:

1) if adopting an optimistic forecast, commit in terms of production to part of that forecast and then plan for the manufacturing and distribution to be reactive to fulfil the existing demand;

2) if adopting a pessimistic forecast, commit to the totality of the forecast and then plan for the manufacturing and distribution to be reactive to fulfil the extra demand;

3) if the manufacturing/ distribution is not reactive enough for whatever reason (for example lead time of the components, manufacturing capacity, etc.), commit to the optimist forecast and use promotional techniques to deploy that stock (price promotion, multi-buy, etc.).

Once again the impact of sampling activity in the HC supply chain was analysed. The period chosen for this simulation were the 2 weeks in beginning of April for the retailer agent only (normally the impact of the sampling in store in other channels is almost negligible), and similar to the approach described in the previous section was applied. The HC supply chain was subjected to three different scenarios:

(i) promotional sampling according to planed promotional plan (scenario A, 200% above normal sales target);

(ii) over performance of the sampling campaign against planned (Scenario B, 400%);

(iii) under performance of the sampling campaign against planned (Scenario C, 87%).

In terms of the optimisation ordering and supply, the parameters used are as shown in Table 7.

Furthermore, for the first simulations there was no increment in the manufactured quantities, and the second simulations the manufacturing increases its production output by 200%, 3 weeks before the stock is required. The results are shown in Table 15. The results show surprisingly that for all three scenarios not altering the original manufacturing plan shows better profit results, as the current stock levels are once again enough to support fluctuations in demand without having OOS – once again it is proven that the built model is robust enough to cope with variations in demand.

WITHOUT MANUFACTURING ADJUSTMENT MANUAL MANUFACTURING ADJUSTMENT

Table 15 Impact of sampling campaign scenarios in the retailer agent.

The current parameters seem to be able to cope with predicted variations, but in order to improve the profitability even further the GA can be applied for all three scenarios. Following the same procedure described in the previous sections, the GA was determined for the scenario A. The results are shown in Table 16. These results show again that by optimising this model the increase in the overall profit increases by 158%, based only on the right ordering quantities.

Profit with promotion

Table 16 Profit values due to sampling promotion for a specific SKU (one year trade), with and without optimised GA parameters.

As a general rule, promotions should not be performed in a period where the demand is already quite high and the manufacturing capacity is already fulfilled with current demand as (1) in order to fulfil the future demand manufacturing will have to start to produce much earlier on which leads to increase in the stock holding costs, (2) it is harder to react if there is an over performance which might lead to out-of-stock situations, (3) if it is a period where sales are quite high, the fact that there is a product in promotion might detract from other products, causing an overstock situation for other products. Interestingly retailers plan their biggest promotional campaigns at trading peaks partially because other retailers are doing the same.