Failed Retail Video Analytics Startup Analyzed

**** ************* ** **** industry *** **** ** cover ** *******.

*** ** ******** ******* backed ** ******* ******'* most *********** *********** *** provided ** **-***** ******* of *** ****** ** their ******. ****** **** note, ** ****** *** analyze ****, ******** *** other ********* ******, *** companies ********** ** ****** video *********.

[***************]

Prayas *********

****** *********, *** ****** *******, was ******** ** ***** a ****** ********* ********. The ******* *** **** of ************** ****** **** ******* batch. ****** ***** ******* is ***** ******, *** company ******* ** * different *********** *** ****** their ****** ********* ******** in **** ****.

**-************** ******************* *** ******* *** the ******* ******, ** * **** detailed *** *********** **** post.

Lessons *******

******* ****** **** ** the **** **** *** particularly ******** ** ******* challenges ** ****** *********:

** *** ****, ***** looking ** *** ****, someone *** ** ****** out **** ** ** and **** ********* *** solution. **** **** *** analytics ******* **** ******, and ********* ******** ** adoption.

**** ** ******* *** of *** ******* ********** for ****** ********* ********. While **** *** ******* metrics ** ******** ****** the *****, ** ***/****** profile ** ********, *** retailer ***** ***** ** decide **** ** ** with **** ****, ***** is *** ****** *******. Further, ** *** ** difficult *** *** ********* products ** ***** ***********, as ********* ***** ** retailers **** ****** ********* target ******* *** ***** layouts. *** ******-**** ***** browsing ** ****** *** 10 ******* * **** sign ** * *** one? ** **, **** do *** ** ** encourage ** ********** ****?

**** ******* **** ********* protection-style *********, ***** *** action **** *** ********* is ******* **** *****-***: determine ** *** ****** is *** ********** ** be ** *** ********, and ** **, ******* to *** **** ** leave *** ******** *** guards, ******, ***.

******* **** *** ******** model *** **** ****** didn’t ****** *** **** like **** *******, *** so ***** *** ** imperfect **********. ***** ********* weren’t **** ** ***** this ****, ** **** them * *** ** figure *** *** ** use **. ** **** couldn’t ****** ** ***, the **** *** *******.

****** ********* ********* *** often ********* **** **** customers *** *** ********** have, ***** *** ******** early ********, *** ** the **** **** ****** the ******* **** *** first ***** *****: ********* do *** **** ********* and ******* ** ***** to *** ** **** data. ** *** **** can *** ** **** in **** *** **** increases *******, ** ***** value.

*** ********, *** ** the ***** ****** ** should’ve **** ***** **** been ** **** ** an ****** ** *** field ** ******** ******* and *** ****** ************. We *****’** ****** ** this ****** ***** *** plans ** *** *** the ***** **** ***** cameras, *** *********** ** they **** ********* ** not. ******* ** **** panicked *** ******* *** a **** ****, *********** between ******* ******* **** it ***** **** *** hopeless **** ** ******’*.

*** ******** ** ****** lacked ******** ******** *********, or **** *********** **** the ******** ********. ***** model *** ***** ** pulling ***** **** ******** surveillance *******, *** **** doing *****-***** ******** ** the ***** ** ******** their ****/*******. *******, **** were *** **** ********* it *** ******** ** retrieve *** ***** **** required, *** **** ********* unaware **** ***** *** a **** ****** ** systems ********, ***** **** unique ***'* *** **********, which **** ** *********** costs.

******* ********* ******** **** infrastructure ******. ** *** case, ** *** ******* with *** ****** ******* and ****** ****** **** typically **** *** * 1 **** ********** ** power ***** *** ******.

******** ********* *** **** a ******** ****** *** many *****-***** ***** ******** in *** ********** *****. Though **** ** ********* bandwidth *** ** ******** better ** **** **** when **** ***** ******** in ****, ** ***** remains * ******** ****** for ***** ********* ** scale *******, ** ******* customers **** **** **** a *** ******* *** location.

** **** ********** ****** by ***** ***** ** progress. ** ****** ******* that ** **** **** close ** ******* *** deal... ***** **** ***** I *** ******* ******* saying **** **’** **** close ** * ******* dollar ******** **** ****. Three ***** ***** — still ** ********!

**** ** * ******* often **** ** ******** in *** ******, ********* customers *** ** ********* say "***", *** ****** to ***** ********, *** they **** *** ***** not ******* ** **** a **** "**" ******. This ******* ** ********* anticipating ******** **** **** never *****, *** ******** resources ** ******** **** opportunities, **** ****** *** would-be ******** ***** ******* and ************. *******, ****** customers ** ********** **** a ********** *** ***** extremely ****-*********, ********* ** spend ***** ** ********** that **** *** ******** improve *****, ****** ******, or ********* **** * direct ******-**** ******.

Deserve ******* *** ********* *** *********

****** ********* *******, *** knowing **** ** **** onto ********* **** ** uncommon, *** **** ** the ******** ********, *** in **** ****** ** well. ******* ********** ******** respect *** *********** *** knowing **** ** **** on *** ****** ********* else. ****** ********* ******* into "*********", ***** **** pivoted ***************** ****** ****, ***** is ***** ********* ****** (though *** ******* ** do **** ******** ** retail *********).

Retail ********* *********** ******

****** ** ********* *** the **** ******* ** find ****** ********* ** be * *********** ******.***** *************** ** **** ** a ****** ********* ******** to ***** ******* ** Silicon ******, *** *** since ******* ******* ** a *******-***** ******** ********* company, ********* ** ***** verticals, **** ** **********, and ******** ******** **** intelligent ******, ** **** as ******* ***** ** analytics *** ******* ******** insight ********. ***********, * retail ********* ********** *** many *****, *** **** ~$2 ******* ** *********** ******** ** ****, *** ** *** extent **** ***** ******** will ******* ***, **** depend ******* ** **** strong ****** ******** ** retail ** *******. ** 2015, ****** ********* ******************** **** ** ********, * ********** ***** VSaaS ********.

**** ****** ********* ********* have ******* ****** ********* applications **********, ******* ********** that ******** ******, *** willingness ** *** *** solutions, ** *** ******** to ******* **** ** perimeter ********** ** *********-******** search ***********.