How data revolution is shaping retail networks today Esri User Conference 2016

Transcription

1 How data revolution is shaping retail networks today Esri User Conference 2016

2 Data revolution is here! PwC 2

3 What does it mean? Did you know Due to the lack of analytical skills companies analyse only 12% of data Over 80% of data in enterprise consists of unstructured data Every year data volumes explode by 40% Poor data can cost businesses 20%-35% of their operating revenue Big Data investments accounted for nearly $40 billion in 2015 alone PwC 3

4 How does it affect retail networks? Data revolution PwC 4

5 Retail Network challenges of today Finding white spots/not covered micromarkets on saturated market to continue network expansion Choosing good locations, ensuring profitability of new shops, when the most obvious locations are already covered Focusing the expansion team s efforts on the most attractive areas, to ensure cost efficient expansion process Improving the results of existing shops by concentrating the efforts on shops performing under their location potential, while taking into consideration diversified demand and fierce competition Choosing poor performing shops to be clos`ed as well as those which have potential to improve results and identifying main triggers of this improvements PwC 5

6 How to make the most of it? Data revolution PwC 6

7 1 Put it all together PwC 7

8 Typical approach. Focus only on geospatial factors or only on internal factors Geodata on one, high level of granularity (zip-codes, municipalities) Static data Focus on clients locations, own branches and competition locations Visual analysis of maps GIS software as the only tool PwC 8

9 our approach is different PwC GeoDataMart + Analytics engine PwC GDM in a nutshell: Econometric modelling All geodata available for certain geography in one database Demographics data Points of interest Granularity adjusted to address point level All internal client s data geocoded and ready to analyse SQL + GeoDataBase formats for interoperability 5 European countries covered, ready to expand further Retail branches Road model Calculated Tables GeoData Mart Client Internal Data Surroundings Own network KPIs External Data.NET platform ESRI GIS BIG DATA architecture Relational database Interactive data visualization Accurate geospatial data and deep retail business knowledge combined with an analytical powerhouse is the base of our methodology PwC 9

10 2 Get to know existing and potential customers PwC 10

11 Mapping customers and their behaviour to determine catchment area of point of sale We divide geography into micromarkets, we map existing customers and identify prospects Profiling of existing customers to perform customer segmentation Identifying prospective clients clusters on the map by locating population with appropriate demographic parameters Mapping competitors to define geographically coherent micromarkets PwC 11

12 3 Understand threats, success and failure factors of existing points of sales PwC 12

13 Examples of internal drivers: Historical sales per SKU Number of counters Employees characteristics Assortment Historical CAPEX We take a deep dive into historical data of the chain in geospatial context to list performance drivers Examples of external drivers: Competitor A within 200m Churches in 1km radius Drive times in rush hours Purchasing power in catchment +65 population in micromarket On recent project we tested over 600 hypotheses PwC 13

14 4 Discover & quantify the impact of all drivers on the network PwC 14

15 We quantify all drivers and micromarkets characteristics and design an econometrical model Internal factors Other available data to be discussed with the client POS effectiveness on the level of particular role Information about employees (competence, profiles, historical performance) Qualitative data (research, NPS, other) Iterative approach to identify variables, their parameters and indicators showing their statistical significance and model forecasting accuracy Econometric modelling resulting in identification of store performance drivers and defining perfect location Outliers analysis for atypically performing stores to identify special conditions and unusual locations Indicators of effectiveness Financial information (OPEX, CAPEX) Basic information POS level (radius 100m, 500m, 1000m) Micromarket level Regional level Country level Chosen KPIs on the POS level POS dynamics on the market Macroeconomic data Competition POI Demographic data Microeconomic data Footfall, traffic External factors PwC 15

16 5 Get a clear, visualized solution on what to do next PwC 16

17 Examples of supporting tools Scoring cards for the branch network (every region, every branch) Optimal locations model (heat map) Client segments location tool (demographic tapestry) Network performance monitoring dashboard Competitors expansion scenario simulator Micromarket sales potential measurement tool We design strategy for the chain base on our model and create geospatial tools to support implementation PwC 17

18 Case study PwC 18

19 Case 1 Card transactions of Polish bank s clients in 2015 PwC 19

20 Case 2 Real shopping gallery catchment area model, based on clients distribution PwC 20

21 Case 3 Postage delivery times from clients to central depot PwC 21

22 Case 4 New production facility scenario analysis Sales value per Client [ K] < > 300 Potential locations Optimal transit route PwC 22

23 Questions? PwC 23

24 Thank you! Łukasz Dziekan Senior Manager Phone: Michał Kliś Senior Associate Phone: This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, PwC Polska sp. z o.o., its members, employees and agents do not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it PwC Polska sp. z o.o. All rights reserved. In this document, PwC refers to PwC Polska sp. z o.o. which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity.