PMI - Retail Survey

Step 1 of 30

1) DEMOGRAPHICS

Please fill in your details below. After completion, you will automatically receive your personalized PMI report with gap analysis and industry benchmark.

1.1) Respondent

Email(Required)

First Name(Required)

Last Name(Required)

Company(Required)

1.2) Company HQ Country

Please specify the country you are based in.

Country(Required)

1.3) What is your company's vertical?

Industry(Required)

1.4) In how many countries is your company active?

How many Markets?

Write a Number

1.5) What is your company type?

Choose one or more Company Type

Brand/D2C

Retailer

Wholesaler

Other

1.6) What were your company's last year's global revenues?

Last year's revenue

<€100M

€100-250M

€250-500M

€500M-€1.5Bn

€1.5-5Bn

€5-10Bn

>€10Bn

1.7) What is your role in your business unit/company?

Role

Category/Product Mgmt.

Buying/Purchasing

Pricing

Controlling

Data Science

Marketing

eCommerce/Digital

Other

1.8) What is your level/hierarchy in your business unit/company?

Hierarchy

Employee

Team Lead

Department Head (Head of)

Division Head (Director/VP/SVP)

Management Board (C-level)

Other/Don't want to answer

1.9) What is the revenue share of the business unit you are responsible for?

Revenue you are responsible for:

<10%

11-20%

21-30%

31-40%

41-50%

51-60%

61-70%

71-80%

81-90%

>90%

1.10) What is your business unit/company's rough online sales share out of total sales?

Rough online sales share

<10%

11-20%

21-30%

31-40%

41-50%

51-60%

61-70%

71-80%

81-90%

>90%

1.11) How many SKUs (roughly) does your business unit have?

How many SKUs?

<1K

1-10K

10-50K

50-100K

100-500K

500K-1.5M

>1.5M

2) PRICING STRATEGY

2.1) Pricing performance

How do you set your goals and performance standard in pricing?

Current level of setting goals and performance standards in pricing(Required)

No goals and performance standards for pricing at all. In ad-hoc meetings and operational steering meetings (such as Weekly Business Reviews), management discusses financial performances and pricing team derives/develops SKU-level ad-hoc measures (e.g., price increases).

Pricing performance is ambiguously defined in KPIs. Performance standards are based on intuition mostly reflecting current necessities. Pricing performance is assessed only in an isolated way not connected to the overall business performance / reviews.

Pricing performance is unambiguously defined in KPIs. Performance standards are based on data but mostly reflect current necessities. Pricing performance is included in low-level and mid-level business reviews.

Performance standards are the results of a systematic process. Underlying drivers are identified to quantify the reasons for deviations between actual and planned performance. Pricing performance is included in all level of business reviews.

Pricing performance is included and consistently linked across all levels of business reviews. Business reviews systematically evaluate current performance compared to desired performance, highlight key drivers of deviation, discuss potential countermeasures, quantify their likely impact, and reevaluate the current status of previously agreed measures.

Ambition level for setting goals and performance standards in pricing(Required)

2.2) Pricing approach

What is your general approach to pricing?

Current approach to pricing(Required)

Mainly manual guidelines (e.g., target margins on category level)

Basic category KPIs as input for manual decision making (e.g., decrease prices due to negative growth of category)

Basic rule-based price recommendations (e.g., 5% less than the cheapest competitor) +...

Advanced data-driven algorithmic price recommendation (e.g., based on price elasticity) +...

AI/ML-enhanced algorithmic price optimization of full product catalogue

Target approach to pricing(Required)

Mainly manual guidelines (e.g., target margins on category level)

Basic category KPIs as input for manual decision making (e.g., decrease prices due to negative growth of category)

Basic rule-based price recommendations (e.g., 5% less than the cheapest competitor) +...

Advanced data-driven algorithmic price recommendation (e.g., based on price elasticity)'+...

AI/ML-enhanced algorithmic price optimization of full product catalogue

3) PRICING TACTICS

To what extent does your pricing approach...

3.1) Competitive pricing

...applies competitive pricing.

Current competitive pricing logic(Required)

No competition-based price logic available

Basic price matching (e.g., min, avg.)

Smart price matching (e.g., min. of competitors that show a positive cross-sensitivity)

... + Competitive scenario detection (e.g., follow minimum competitor of the relevant ones)

... + "Race to the bottom" detection & anticipation

Target competitive pricing logic(Required)

No competition-based price logic available

Basic price matching (e.g., min, avg.)

Smart price matching (e.g., min. of competitors that show a positive cross-sensitivity)

... + Competitive scenario detection (e.g., follow minimum competitor of the relevant ones)

... + "Race to the bottom" detection & anticipation

3.2) Revenue

...support revenue maximization

Current revenue maximization approach(Required)

No revenue-maximizing pricing logic available

Revenue optimization based on category performance vs. targets

Based on revenue targets on SKU-level / cluster level

Identification of revenue-maximizing price using price elasticity

... + Consideration of contextual factors (e.g., seasonality)

Target revenue maximization approach(Required)

No revenue-maximizing pricing logic available

Revenue optimization based on category performance vs. targets

Based on revenue targets on SKU-level / cluster level

Identification of revenue-maximizing price using price elasticity

... + Consideration of contextual factors (e.g., seasonality)

3.3) Profit

...support profit optimization

Current profit optimization approach(Required)

No profit-optimizing pricing logic available

Based on (target) margins per category

Based on (target margins) on SKU-level / cluster-level

Identification of profit-maximizing price along profit-parabola using price elasticity

... + Consideration of contextual factors (e.g., seasonality)

Target profit optimization approach(Required)

No profit-optimizing pricing logic available

Based on (target) margins per category

Based on (target margins) on SKU-level / cluster-level

Identification of profit-maximizing price along profit-parabola using price elasticity

... + Consideration of contextual factors (e.g., seasonality)

4) PRICING INSIGHTS & ANALYTICS

To what extent does your pricing approach...

4.1) Article segmentation

...leverage articles segmentation (KVIs/ lighthouse articles, sales driver, profit generators, long tail)

Current level of sophistication for article segmentation(Required)

Concept not used

Implicitly applied by category managers when taking price decisions

Centrally embedded using qualitative assessment (e.g., input from category managers)

Centrally applied using quantitative assessment (e.g., based on SKU-level price elasticity)

Centrally applied using AI/ML-powered scoring model

Target level of sophistication for article segmentation(Required)

Concept not used

Implicitly applied by category managers when taking price decisions

Centrally embedded using qualitative assessment (e.g., input from category managers)

Centrally applied using quantitative assessment (e.g., based on SKU-level price elasticity)

Centrally applied using AI/ML-powered scoring model

4.2) Relevant competitor identification

...help to identify your main competitors

Current approach to competitor selection & prioritization(Required)

No competitor prices are taken into account in the pricing tool

All known competitors are taken into account

Competitors are manually select based on multiple factors (e.g., assortment overlap, pricing behavior)

Advanced data-driven selection leveraging mechanisms (e.g., how competitor prices influence own volumes)

Fully automated, advanced data-driven selection on SKU-level

Target approach to competitor selection & prioritization(Required)

No competitor prices are taken into account in the pricing tool

All known competitors are taken into account

Competitors are manually select based on multiple factors (e.g., assortment overlap, pricing behavior)

Advanced data-driven selection leveraging mechanisms (e.g., how competitor prices influence own volumes)

Fully automated, advanced data-driven selection on SKU-level

4.3) Price elasticity

...leverage price elasticity

Current approach to dealing with price elasticity(Required)

Concept not implemented

Price elasticity is measured periodically, but not on SKU level

Price elasticity is measured periodically on 'bucket' level per SKU (high/medium/low)

Price elasticity is measured on the most important articles and extrapolated to the rest of the category

Price elasticity is measured continiously and per SKU level

Target approach to leverage price elasticity(Required)

Concept not implemented

Price elasticity is measured periodically, but not on SKU level

Price elasticity is measured periodically on 'bucket' level per SKU (high/medium/low)

Price elasticity is measured on the most important articles and extrapolated to the rest of the category

Price elasticity is measured continiously and per SKU level

4.4) Seasonality

...consider seasonality

Current way of dealing with seasonality(Required)

Seasonality is not taken into account

Manual seasonality detection and flagging

Data-driven seasonality detection and flagging

Automated seasonality detection and flagging

Automated seasonality detection and consideration for price calculation

Target way of dealing with seasonality(Required)

Seasonality is not taken into account

Manual seasonality detection and flagging

Data-driven seasonality detection and flagging

Automated seasonality detection and flagging

Automated seasonality detection and consideration for price calculation

4.5) Demand Forecasting

...leverage demand forecasting

Current use of demand forecasting(Required)

No demand forecasting available / taken into account

Manual demand forecasting on cluster level (e.g., by category manager)

Data-driven demand forecasting based on historical/seasonal data on cluster level (eg. category level)

Data-driven demand forecasting based on historical/seasonal data on SKU-level

Data-driven demand forecasting corrected by contextual data (competitive actions, weather, events, etc)

Target way of dealing with seasonality(Required)

No demand forecasting available / taken into account

Manual demand forecasting on cluster level (e.g., by category manager)

Data-driven demand forecasting based on historical/seasonal data on cluster level (eg. category level)

Data-driven demand forecasting based on historical/seasonal data on SKU-level

Data-driven demand forecasting corrected by contextual data (competitive actions, weather, events, etc)

4.6) Stock Forecasting

...leverage stock forecasting

Current use of stock forecasting(Required)

Stock forecasting is not available / taken into account

Manual stock forecasting on cluster level (e.g,. supplier-level)

Data-driven stock forecasting based on historical data on cluster level (ie category)

Data-driven stock forecasting based on historical data on SKU level

Data-driven stock forecasting based on data driven demand forecasting on SKU level

Ambition level for stock forecasting(Required)

Stock forecasting is not available / taken into account

Manual stock forecasting on cluster level (e.g,. supplier-level)

Data-driven stock forecasting based on historical data on cluster level (ie category)

Data-driven stock forecasting based on historical data on SKU level

Data-driven stock forecasting based on data driven demand forecasting on SKU level

5) PRICING CONFIGURATION

5.1) Price guardrails & psychological pricing - (Please select all that apply)

...integrate price guardrails and consider psychological prices

Current guardrails taken into account(Required)

Recommended Retail Price (where available)

Margin floor / min. margin

Price stability period/duration

Max. price change/steps

Psychological price points (i.e., smart rounding, price barriers, magic number 9, etc.)

Target guardrails to be considered(Required)

Recommended Retail Price (where available)

Margin floor / min. margin

Price stability period/duration

Max. price change/steps

Psychological price points (i.e., smart rounding, price barriers, magic number 9, etc.)

5.2) Price alignment - (Please select all that apply)

...ensure price alignment

Current possibilities for price alignment(Required)

pack size architecture (e.g., (price per ml or kg)

Bundles or multi-packs (price per unit)

Price tier architecture (e.g., good-better-best brands)

Product families (e.g., sub-brands)

Product styles (e.g., colors)

Target possibilities for price alignment(Required)

pack size architecture (e.g., (price per ml or kg)

Bundles or multi-packs (price per unit)

Price tier architecture (e.g., good-better-best brands)

Product families (e.g., sub-brands)

Product styles (e.g., colors)

6) PRICING SYSTEM & DATA

How does work your pricing approach?

6.1) Pricing tool

How would you describe your company's (or your business unit's) pricing tool?

Current pricing tool(Required)

No dedicated pricing tool available

We have a separate/external calculation tool (e.g., XLS) to calculate prices for manual data entry

We have an integrated pricing tool for manual activation

We have an integrated pricing tool (external or internal tool) for manual activation

We have an integrated pricing tool for fully automated price activations

Target pricing tool(Required)

No dedicated pricing tool available

We have a separate/external calculation tool (e.g., XLS) to calculate prices for manual data entry

We have an integrated pricing tool for manual activation

We have an integrated pricing tool (external or internal tool) for manual activation

We have an integrated pricing tool for fully automated price activations

6.2) Decision support - (Please select all that apply)

How do your pricing capabilities support price management and pricing decisions?

Current level of decision support(Required)

Different user roles & approval workflow provided

Possibility to assign tasks and communicate between users

Steering dashboards included

Simulation of different strategies supported: visualizing the expected/simulated impact of a new strategy without actually changing the price

A/B Testing UI: supporting in selecting treatment & control group and measuring the impact after changing the price

Target level of decision support(Required)

Different user roles & approval workflow provided

Possibility to assign tasks and communicate between users

Steering dashboards included

Simulation of different strategies supported: visualizing the expected/simulated impact of a new strategy without actually changing the price

A/B Testing UI: supporting in selecting treatment & control group and measuring the impact after changing the price

6.3) Pricing data - (Please select all that apply)

What data sources are available for the pricing tool / decision making?

Currently available/connected data sources(Required)

Competitor prices

Stock/inventory data

Historical transaction data

Behavioral information (e.g., web analytics such as views, clicks, conversions)

Context data (e.g., weather, day of the week, relative position within a month)

Target data sources(Required)

Competitor prices

Stock/inventory data

Historical transaction data

Behavioral information (e.g., web analytics such as views, clicks, conversions)

Context data (e.g., weather, day of the week, relative position within a month)

7) PRICING ORGANIZATION & GOVERNANCE

7.1) Organization setup

How is your pricing organized?

Current organizational setup(Required)

No formal pricing team installed, pricing decisions are part of the category manager's role

A pricing and data anlytics team is installed - but the role is mainly responsible for data analytics and working in an advisory role

A pricing and data analytics team is installed with full responsability over the price and promo decisions

A pricing team is installed, working together with an installed data science team (that supports both pricing, category mgt and marketing)

Pricing role is present in leadership team and reports directly to the CEO/GM

Target organizational setup(Required)

No formal pricing team installed, pricing decisions are part of the category manager's role

A pricing and data anlytics team is installed - but the role is mainly responsible for data analytics and working in an advisory role

A pricing and data analytics team is installed with full responsability over the price and promo decisions

A pricing team is installed, working together with an installed data science team (that supports both pricing, category mgt and marketing)

Pricing role is present in leadership team and reports directly to the CEO/GM

8) USE CASE SUPPORT

8.1) Data-based pricing decisions

How important do you think data-driven decision-making in pricing is to your company's success?

Not important at all	Slightly important	Somewhat important	Moderately important	Important	Very Important	Extremely important

"In our company, pricing decisions should be made more on the basis of data."

Strongly disagree	Disagree	Somewhat disagree	Neutral	Somewhat agree	Agree	Strongly agree

"In our company, pricing decisions are predominantly made on the basis of data."

Strongly disagree	Disagree	Somewhat disagree	Neutral	Somewhat agree	Agree	Strongly agree

"In our company, all the necessary data is available for making pricing decisions."

Strongly disagree	Disagree	Somewhat disagree	Neutral	Somewhat agree	Agree	Strongly agree

"In our company, the technical support for pricing decisions is sufficient."

Strongly disagree	Disagree	Somewhat disagree	Neutral	Somewhat agree	Agree	Strongly agree

9) Company Performance

Please describe...

...the overall performance of your firm/business unit last year.

Very poor	Poor	Slightly poor	Average	Good	Very good	Excellent

...the overall performance of your firm/business unit relative to major competitors last year.

Very poor	Poor	Slightly poor	Average	Good	Very good	Excellent

...the performance of the firm/business unit relative to major competitors with respect to PROFIT.

Much worse than competitors	Worse than competitors	Slightly worse than competitors	On par with competitors	Slightly better than competitors	Better than competitors	Much better than competitors

...the performance of the firm/business unit relative to major competitors with respect to PROFIT GROWTH.

Much worse than competitors	Worse than competitors	Slightly worse than competitors	On par with competitors	Slightly better than competitors	Better than competitors	Much better than competitors

...the performance of the firm/business unit relative to major competitors with respect to PROFIT MARGIN.

Much worse than competitors	Worse than competitors	Slightly worse than competitors	On par with competitors	Slightly better than competitors	Better than competitors	Much better than competitors

...the performance of the firm/business unit relative to major competitors with respect to REVENUE.

Much worse than competitors	Worse than competitors	Slightly worse than competitors	On par with competitors	Slightly better than competitors	Better than competitors	Much better than competitors

...the performance of the firm/business unit relative to major competitors with respect to REVENUE GROWTH.

Much worse than competitors	Worse than competitors	Slightly worse than competitors	On par with competitors	Slightly better than competitors	Better than competitors	Much better than competitors

Name

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