Data through the retail value chain: today’s possibilities

Retail Executive, Matthew Burden discusses why data-led innovation will be the key to sustainable retail operations and success in winning consumers’ hearts, minds, and wallets.

By Matthew Burden

It’s no secret that   Australian  retailers are facing a challenging environment.  First,  consumer spending  is  both  cautious  and  moving online.  Simultaneously,  department  stores  and  high  street  brands  are  facing  significant  headwinds,  with  a  growing  list  of  international  brands  coveting  the  potential  of  this  market,  bringing  new  ideas  and  technology  to  a  customer  base  hungry  for  disruption.  

We are now well and truly in the intelligence age; promising a more efficient, convenient and personalised world and no organisation will be left unchanged.   

In this environment, retailers must be brave – making conscious decisions to experiment, to invest  and  measure,  to  try  and sometimes, to fail. Many will have to disrupt themselves before someone else does. They must be willing to let data and technology fundamentally transform the way they do business.   

I firmly believe data-led  innovation  will  be  the  key  to  sustainable  retail  operations  and  success  in winning  consumers’  hearts,  minds,  and  wallets.  In  this  new  world,  the  ability  to  make  decisions  smartly,  consistently  and  quickly  –  as  well  as  to measure  and  conclude  what  to  continue  or  stop  –  will  be  the  difference  between  organisations  that  continue  to  grow,  and  those  that  ultimately  fail.  Retail  is  a  zero-sum  game,  and  data  is  the  differentiator.  

To  really  embrace  this  potential,  retailers  should  look  to  fully  embed  data-led  solutions  into  the  value  chain  wherever  they  can  add  value.  We’re  talking  about  decision  engines –  algorithmic  solutions  that  transform  how  we  go  about making  and  measuring  decisions,  based  on  all  the  available  relevant  data,  at  great  speed  and  that  are  constantly  learning  to improve  effectiveness.  Data  science  and  AI  are  making  this  kind  of  all-encompassing  decision  engine  possible.   

These  engines  are  created  and  pointed  at  specific  challenges  in  your  value  chain.  We  work  with  retailers  to  find  the  opportunities  for  data  to  intervene  in  major  decision-making  processes  to  improve  operations  and  customer  experience.  

There is a human aspect to  retail, an  art  of  experience  and  decision  making.  We  can  take  the  amazing  capabilities  and  experiences  that  already  exist  in  retailers  and  amplify  them  through  data  science  and  AI.   While   a  full  data  revolution  across all  aspects  of  the  retail  value  chain  is  still  a  little  way  into  the  future,   individual  parts  of  it  are   already  being  significantly  transformed  with  data-driven  decision engines.  

Personalisation 

Today, it  is  possible  (in  a  retail  sense)  to  personalise  everything –  assortment,  offer,  media,  channel  and  timing.  It   is   possible  to  predict  what  a  specific,  individual  customer  (or  group of  customers)  will  want  to  buy,  when  they’re  likely  to  buy it,  from  where,  at  what  price  or  level  of  offer  and  how  best  to  reach  them  to  stand  the  best  chance  of  converting  to  purchase.  And  I  really  mean  that  individual  person  –  not  a  lookalike  from  a  broader  homogeneous  group –  but  a  genuine ‘segment  of  one’.  Very  few  retailers  are  maximising  the  potential  of  this  personalisation,  yet  it  ensures  true  relevance  to  the  customer  and  greater  effectiveness  of  investment  in  offers,  channels  and  media.  (Read  more  about  some  of  our  media targeted  media  segments  here.) 

Forecasting 

Parts  of  the  retail  industry  have  come  a  long  way  from  instinctual  and  experiential  decision  making,  but  many  still  have  a  long  way  to  go.  It’s  now  possible  to  achieve  automated,  accurate  demand  planning  based  on  the  aggregation  of  predicted  individual  behaviours  along  with  hundreds  of  factors  like  weather,  economic  conditions and  calendar  events.  We  can  ingest,  sort and  interpret  vast  data  sets  quickly  and  efficiently.  Retailers can  optimise  stock  levels  by  accurately forecasting  demand  for  individual  SKUs,  helping  to  eliminate  lost  sales  and  driving  down  waste.  (Read  more  about  AI  based  forecasting  here .) 

Combining range and space decisions 

Data  science  and  AI  has  demonstrated  an  intrinsic  link  between  space  and  product  assortment,  and  we’re  now  able  to  realise  the  potential  of  combined  range  and  space  decisions;  optimising  return  on  the  space  from  an  individual  SKU  in  an individual  store.  Moving  from  aggregated  segments  again  to  a  ‘segment  of  one’,  we  can  create  store  ranges  that  are  accurately  tailored  to  local  customer  needs.  As  an  ‘always  on’  decision  engine,  we’re  challenging  the  annual  range  review cycle  –  algorithms  can  know  when  a  category  needs  reviewing,  complete  the  review,  and  apply  relevant  changes  to  relevant  stores  without  the  need  for  external  prompts.  (Read  more  about  breaking the range paradigm   from  our  range  expert Jennifer  Dimić.) 

Looking forward 

In  the  future,  AI  and  algorithms  will  support  all  aspects  of  the  retail  operation  and  will  be  able  to  tie  them  all  together;  recommending  the  right  stock,  seamlessly  moving  it  through  to  the  right  parts  of  the  business  at  the  right  speed  to  fulfil  the  right  customer  needs,  through  the  right  channel  at  the right  time.  They  will  continuously  measure  and  learn adapting  the  business  and  optimising  the  offer  to  the  ever-changing needs  of  customers.   

We’re  not  there  yet,  however  retailers  who  are  building  decision  engines  like  these  now  and  applying  them  at  individual  points  throughout  the  value  chain  are  reaping  the  rewards.  Businesses  not  fully  embracing  data’s  potential  are  leaving  a huge  amount  of  value  on  the  table,   and  risk  being  left  behind.  

At  Quantium,  we’ve  seen  five-fold  improvements  in  results  from  machine  learning  approaches  in  personalised  communication  and  double-digit  improvements  in  forecasting  accuracy  through  tailoring  decision  engines  to  the  needs  of  our clients.  If  you’re  ready  to  explore  the  potential  for  data  science  and  AI  to  transform  your  value  chain, we’d  be  delighted  to  speak  with  you.

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